Design and Implementation of a Simulator Based on a Cross-Layer Protocol between MAC and PHY Layers in a WiBro Compatible IEEE 802.

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1 CROSS-LAYER DESIGN Design and Implementation of a Simulator Based on a Cross-Layer Protocol between and Layers in a WiBro Compatible IEEE e OFDMA System Taesoo Kwon, Howon Lee, Sik Choi, Juyeop Kim, and Dong-Ho Cho, Korea Advanced Institute of Science and Technology Sunghyun Cho, Sangboh Yun, Won-Hyoung Park, and Kiho Kim, Samsung Advanced Institute of Technology ABSTRACT In this article we propose cross- design frameworks for e OFDMA systems that are compatible with WiBro based on various kinds of cross- protocols for performance improvement: a cross- adaptation framework and a design example of primitives for cross- operation between its and s. In addition, we provide a simulation framework for cross- analysis between the and s in e systems. Through this cross- simulator, we show that average cell throughput can be improved by percent by applying careful cross- adaptation schemes. INTRODUCTION The IEEE family of standards specifies the air interface of fixed and mobile broadband wireless access (BWA) systems that support multimedia services. A WiMAX system is one based on technologies of this family, sponsored by an industry consortium called the WiMAX Forum. The IEEE standard, which was also previously called d or REVd, was published for fixed access in October Good overviews of the standard can be found in [1, 2]. The standard has now been updated and extended to the e standard for mobile access, Mobile WiMAX, as of October In Korea, HPi, which means high-speed portable Internet, was first conceived as a Korean technology standard to provide better data handling than that of the third-generation (3G) cellular system before fourth-generation (4G) systems arrive, but was renamed WiBro, which means wireless broadband, once it adopted e as its standard for global harmonization. WiBro is designed to provide all-ip and packet services, such as streaming video and music, video and music on demand, online gaming, and broadcasting, over the 2.3 GHz spectrum at ground speeds up to about 60 km/h, and is currently compatible with the e orthogonal frequency-division multiple access (OFDMA) system with a 1024 fast Fourier transform (FFT) size. WiBro services are expected to be launched commercially by two Korean operators in the middle of Once the launch is successful in Korea, it may prompt the successful launching of WiMAX services worldwide. The standard defines the specifications related to the convergence sub (CS), medium access control (), and physical (). Its supports four physical modes: WirelessMAN-SC for any applicable frequencies between 10 and 66 GHz, Wireless- MAN-SCa for licensed frequencies below 11 GHz, WirelessMAN-OFDM for orthogonal frequency-division multiplexing, and Wireless- MAN-OFDMA. In this article we mainly discuss the WirelessMAN-OFDMA system because it is compatible with the WiBro system. With respect to implementation, we need to design inter operations carefully. With this in mind, we present various cross- protocols for performance improvement and a design example of primitives for cross- operation between and s, after first introducing a logical frame structure for OFDMA systems. Moreover, to verify the performance improvement of the cross /05/$ IEEE IEEE Communications Magazine December 2005

2 DL PUSC (mandatory) FUSC OFUSC, or AMC UL PUSC (mandatory) OPUSC or AMC OFDMA symbol number Time k k+1 k+2 k+3 k+m k+m+1 k+n Subchannel logical number s s+1 s+l Preamble FCH DL-MAP UL-MAP CQICH, ACK CH fast feedback CH Ranging DL subframe TTG UL subframe RTG Slot Slot Slot One subchannel Slot PUSC: Partial usage of subchannels FUSC: Full usage of subchannels OP(F)USC: Optional P(F)USC AMC: Adaptive modulation and coding Slot DL PUSC: two OFDMA symbols DL FUSC: one OFDMA symbol UL PUSC: three OFDMA symbols DL/UL AMC: two, three or six OFDMA symbols FCH: Frame control header CQICH: Channel quality information channel TTG: Transmit/receive transition gap RTG: Receive/transmit transition gap n Figure 1. Example of IEEE TDD frame structure. framework presented in this article, we present a framework for designing a simulator for cross analysis between the and in a WiBro or e system, and discuss its performance. FRAME STRUCTURE OF OFDMA SYSTEMS IEEE systems can support time-division duplexing (TDD) and frequency- division duplexing (FDD). The frame can be composed of several zones that are divided according to subcarrier allocation methods or MIMO modes. Figure 1 shows an example of an IEEE TDD frame structure, which is also a model frequently referred to when constructing WiBro systems that support only TDD. In an FDD frame structure, the downlink (DL) and uplink (UL) subframes are allocated in a different frequency band without guard time such as TTG and RTG. The frame structure consists of the following: a preamble using the first symbol, FCH with fixed size for resource allocation of the DL PUSC zone and DL_MAP, DL_MAP and UL_s for resource allocation of DL and UL data bursts, DL/UL data bursts for data or control messages, and UL control channels for ranging, UL acknowledgment (ACK), and CQI feedback. The DL_MAP can present resource allocation information for each burst or each user. Presenting the MAP information for each burst decreases the number of DL_MAP IE messages, but causes processing overhead because a mobile station (MS) needs to find its own packet among many packets concatenated into a burst. Presenting for each user can allocate resources to each user effectively, but causes considerable overhead due to the transmission of many DL_MAP IE messages. So the system defines various s, such as compressed and compact MAP, that reduce the size of MAP messages or allocate resources effectively for each user. To support various types of physical channel condition, IEEE OFDMA systems define two types of subchannel building method: the distributed subcarrier permutation mode (PUSC, OPUSC, FUSC, or OFUSC mode in Fig. 1) and the adjacent subcarrier permutation mode (AMC mode in Fig. 1). The ratio of these modes can be flexible in the IEEE standard. However, one burst for data transmission consists of several slots, and one slot is the minimum possible data allocation unit. In addition, the definition of this slot depends on the OFDMA symbol structure, which varies for DL and UL, for FUSC and PUSC, and for distributed subcarrier permutations and adjacent subcarrier permutation, as shown in Fig. 1. IEEE Communications Magazine December

3 In terms of maximizing the system throughput, max CIR is the best scheduling scheme such that subcarriers are allocated for only users with the best channel conditions. However, for the, required QoS as well as system throughput should be satisfied. DIVERSITY SUBCHANNELS The distributed subcarrier permutation mode is a very useful scheme for averaging intercell interference and avoiding deep fading by selecting subcarriers pseudo-randomly. Therefore, it is expected to be suitable for users with high velocity and/or low signal-to-interference-plus-noise ratio (SINR). Basic resource units in the frequency domain of this mode are called diversity subchannels. BAND AMC SUBCHANNELS In adjacent subcarrier permutation mode, adjacent subcarriers are grouped into clusters and are allocated to users. In this channel structure, the channel response can be seen as a flat fading channel. Thus, frequency selectivity of the channel cannot be exploited. Due to the flat fading nature of this sub-channel, the system can make better use of multiuser diversity as long as the channel state does not change significantly during the scheduling process. Therefore, it is expected to be suitable for users with low velocity and/or high SINR. Basic resource units in the frequency domain of this mode are called band AMC subchannels. CROSS-LAYER PROTOCOL DESIGN BETWEEN E LAYER AND The e standard provides a number of UL control channels for the fast exchange of information for cross- operation. We first introduce some UL control channels, and then present: A cross- adaptation framework for inter operation between and s A design example of primitives for exchanging information for cross- protocol operation We also discuss cross- issues regarding the hybrid automatic repeat request (HARQ) protocol as another important link adaptation technique for e systems. UPLINK CONTROL CHANNELS FOR A CROSS-LAYER PROTOCOL We can design cross- protocols efficiently and improve system performance by carefully utilizing the uplink control channels to exchange cross- information such as physical channel information and ACK/negative ACK (NACK) for HARQ. CQICH The channel quality information channel (CQICH) is allocated to an MS using a CQICH control IE, and is used to report the DL carrier-to-interference-plus-noise ratio (CINR) for either diversity subchannels or band AMC subchannels. This channel occupies one UL slot in the FAST-FEEDBACK region allocated through UL_. For diversity subchannels, the MS reports the average CINR of the BS preamble from which the BS is able to determine the DL modulation and coding scheme (MCS) level. Here, a CINR measurement is quantized into 32 levels and encoded into five information bits. On the other hand, for band AMC subchannels, a mobile station (MS) can report the differential of CINR values of five selected frequency bands (increment: 1 and decrement: 0 with a step of 1 db) on this CQICH after reporting the CINR measurements of the five best bands using a management message such as REP-RSP. Fast Feedback Channels Fast feedback channels may be allocated individually to MSs for the transmission of -related information that requires a fast response from the MS. One fast feedback channel occupies one UL slot in the FAST-FEEDBACK region allocated through a UL_. Using these fast feedback channels, the MS can report the followings: Variable information for operation, such as the anchor BS selection information for macro diversity handover and the request for UL rate adaptation of VoIP service -related information, such as DL channel measurement information for multipleinput multiple-output (MIMO )operation, the MIMO coefficient for the best DL reception (e.g., antenna weight), and MIMO mode selection (e.g., space-time transmit diversity [STTD], spatial multiplexing [SM], and beamforming). UL ACK Channel A HARQ ACK channel region for the inclusion of one or more ACK channel(s) for HARQ support of MSs is allocated using a HARQ ACK region allocation IE. The UL ACK channel occupies one half-slot in this HARQ ACK channel region, which may override the fast feedback region. This UL ACK channel is implicitly assigned to each HARQenabled burst according to the order of the HARQ-enabled DL bursts in the DL MAP. Thus, the MS can quickly transmit ACK or NACK feedback for DL HARQ-enabled packet data using this UL ACK channel. UL Sounding The e OFDMA system defines UL sounding to support smart antenna or MIMO, and this UL sounding is a kind of UL pilot signal. The BS measures the UL channel response from UL sounding waveforms transmitted by each MS, and translates the measured UL channel response to an estimated DL channel response under the assumption of TDD reciprocity. In order to allocate resources for the transmission of UL channel sounding, the BS allocates a sounding zone through a UL_MAP message. In this sounding zone each MS can transmit its UL sounding signal, maintaining signal orthogonality among multiple multiplexed MS sounding transmissions. CROSS-LAYER ADAPTATION FOR EFFICIENT RESOURCE ALLOCATION In OFDM systems [3] the channel gains of subcarriers are quite different, due to the frequency selectivity of the channel. In terms of maximizing 138 IEEE Communications Magazine December 2005

4 -d flows User 1 User 2 User 3 User N-1 User N Diversity user group Diversity user data QoS classes Best effort Diversity channel Resource controller User grouper Scheduler Resource controller AMC user group AMC user data QoS classes Best effort AMC channel Resource controller -c flow ( control information) control information In the proposed model, the contains a user grouper, scheduler, and resource controller. Each functional entity exploits information to increase system throughput. The physical consists of a diversity channel PPDU controller, AMC channel PPDU controller, control information controller, and HARQ functional blocks. HARQ Diversity channel PPDU controller HARQ AMC channel PPDU controller Control information controller HARQ: Hybrid automatic repeat request PPDU: Physical protocol data unit n Figure 2. Cross adaptation scheme for efficient resource allocation. the system throughput, max carrier-to-interference ratio (CIR) is the best scheduling scheme such that subcarriers are allocated for only users with the best channel conditions. However, for the, required quality of service (QoS) as well as system throughput should be satisfied. To maintain high system performance of both s, it is necessary to design a - cross- optimized resource allocation scheme. Scheduling and subchannel allocation algorithms are two such widely accepted key functions in the to exploit the cross information and improve performances. Recently, many ideas have been proposed to address the problem of cross- resource allocation; see [4 8]. The proportional fair (PF) algorithm [4] also considers - issues, such as channel condition and fairness. In this section we propose a - cross- adaptation model for efficient resource allocation in e systems. Figure 2 shows the proposed - cross- adaptation functions and corresponding information flows. In the proposed model the contains a user grouper, scheduler, and resource controller. Each functional entity exploits physical information to increase system throughput. The physical consists of a diversity channel PPDU controller, AMC channel PPDU controller, control information controller, and HARQ functional blocks. In the proposed model AMC subchannel users and diversity subchannel users are classified by the user grouper. Since the properties of AMC subchannels and diversity subchannels are quite different, the grouping of users into two channel types is essential if system throughput is to be increased. The scheduler determines the scheduling of users and the quantity of packets that should be scheduled in the current frame. For cross- optimization, the scheduler should be designed to exploit not only information but also application information. Utility-function-based scheduling [6, 7] is IEEE Communications Magazine December

5 Application- information is gathered by the -c controller and used in the scheduler. With respect to implementation, primitives between the,, and application s should also be defined to support efficient cross- integration. one of the representative methods for resolving this problem. Consider a system consisting of N users, and let U k (x) denote the utility function of user k. In addition, let R k,i,j denote the transmission rate of user k if it is scheduled on the jth subchannel of the ith frame. Then the scheduler can select the users to be served on the ith frame based on the following rule: k* = arg max Uk ( x) Rk,, i j, (1) k {, 1, N} where U k (x) can be considered the priority of user k in the current scheduling frame [6]. We can define U k (x) based on our own scheduling criteria, which rely on not only and requirements, but also application requirements. For example, let U k (x) = log x where x = T k and T k denotes the mean data rate of user k over a certain time period; then the right term of Eq. 1 can be rewritten as arg max k {1,,N} (R k,i,j /T k ), which becomes equivalent to the proportional fair scheduling rule. If U k (x) = a i x 2, where x means the packet delay and a i denotes a positive weighting factor, the scheduling rule becomes equivalent to the modified largest weighted delay first (M-LWDF) rule [6]. Similarly, we can define a novel utility-function-based scheduling rule that can satisfy the QoS requirements of the application and optimize - cross- performance. Once the scheduler determines the scheduled users, the resource controller assigns frequency bands to each selected user in order to maximize the throughput. A subcarrier allocation algorithm can be applied to this resource controller. A subcarrier allocation algorithm performs an important role in optimizing - cross- performance. Therefore, it should be able to exploit physical information such as SINR, MCS level, and velocity. If we also consider the cross- optimization between the and application s, the subcarrier allocation algorithm should satisfy such application requirements as delay bound and minimum data rate. However, the scheduler already considers the application requirements, and determines the users and the amount of resource that should be allocated for each of them in the current frame. Therefore, the problem that should be solved in the subcarrier allocation algorithm can be reduced to the - cross- optimization problem. We can model this optimization problem simply as follows. We assume that each frame consists of M time slots whose length is T ms, and each timeslot is divided into S subchannels in the frequency domain. Then we can obtain the following optimization problem: CROSS-LAYER PROTOCOL FOR HARQ The e system optionally provides the combining gain by incremental redundancy. HARQ is a very important technique for link adaptamaximize subject to k N = 1 S j= 1Rk, jmk, jt S j= 1Rk, jmk, jt Bk, for k (2) k N = 1mk, j= M for j mk, j { 012,,,, M} for k, j, where R k,j denotes the maximum achievable rate of user k on the jth subchannel and m k,j denotes the number of slots that are assigned to user k on the jth subchannel. In addition, B k denotes the amount of data user k should transmit in the current frame. In the proposed model the resource controller determines the value of R k,j based on the CQI feedback information, such as average SINR or MCS level. When the minimum packet requirement is too strict compared to the current channel condition, there may exist no solutions that meet the minimum packet requirements. In this case we can choose the user whose channel condition is worst or priority is lowest, and exclude this user from the scheduling list in the current frame. The functional entities, such as user grouper, scheduler, and resource controller, gather and control information from the control information controller and -c controller, respectively. The control information controller generates and manages such control information as channel matrix, SINR, MCS level, velocity, and location. The -c controller is a function block and controls control information (e.g., fairness) and QoS. In addition, application information is gathered by the -c controller and used in the scheduler. With respect to implementation, primitives between the,, and application s should also be defined to support efficient cross- integration. CROSS-LAYER PROTOCOL FOR CQI FEEDBACK In OFDMA-based systems, the condition of UL and DL channels should be considered when scheduling for the purpose of increasing throughput. For this reason, IEEE defines variable UL control channels, as described earlier. Based on these channels, we propose a cross protocol for CQI feedback, as shown in Fig. 3. Figure 3 shows - primitives, the cross- protocol sequence of the CQI feedback for DL channel measurement, and the UL sounding signal for UL channel measurement. All AMC subchannel users that have a transport connection identifier (CID) should periodically transmit a DL channel measurement report on CQICH. To construct a CQI feedback message, the needs to receive channel measurement results from the physical. Primitives such as CQI-MSG.request and CQI-MSG. response in the DL CQI feedback of Fig. 3 are used for this purpose. Once a CQI feedback message is constructed in an MS, it is transmitted to a BS through CQICH. This information is exploited during scheduling and resource allocation. The UL sounding in Fig. 3 shows the transmission sequence of the UL sounding signal. UL sounding is a kind of UL pilot signal and is defined to support smart antenna or MIMO in e. If an MS confirms its sounding channel allocation in a UL_, an MS sends a SOUNDING.request primitive to an MS. Then an MS sends a sounding signal on the allocated UL sounding region. A BS can use the received sounding signal to measure the quality of the UL channel and translate the measured UL channel quality to an estimated DL channel quality under the assumption of TDD reciprocity. 140 IEEE Communications Magazine November 2005

6 DL CQI feedback Identify CQI channel MS CQI-MSG.request (CQI message mode) CQI-MSG.response (CQI message mode) CQI message (CQI message mode) CQI message (CQI channel) BS CQI message In order to model a physical channel for cross- analysis between e and s, we consider path loss, log-normal shadowing and frequency-selective Rayleigh fading, according to each user s mobility UL sounding MS BS Identify CQI channel CQI-SOUNDING.request (channel sounding mode) Sounding signal (sounding region) CQI-SOUNDING.indication n Figure 3. Cross- protocol for DL CQI feedback and UL sounding. tion, and makes aggressive decisions possible at the MCS level. Thus, the use of HARQ can result in considerably increased throughput [10]. However, it is a critical issue to decide the MCS level and packet size for the original transmission of a HARQ-enabled connection. Extensive retransmissions cause considerable overhead, because HARQ retransmission requires control messages such as Compact_DL/UL MAP_IE in e systems. Thus, it is necessary to consider a trade-off between overhead caused by control messages and efficiency of link adaptation. Recently, various schemes have been studied that attempt to optimize ARQ performance by applying a channel-aware scheduling algorithm to the HARQ retransmission packet. System performance can be improved through a HARQ-aware scheduler design [11]. However, when HARQ is applied to real-time service, we may carefully design retransmission strategies that consider service delay bound as well as channel quality. DESIGN AND IMPLEMENTATION OF A SIMULATOR BASED ON CROSS-LAYER OPTIMIZATION OF E SYSTEMS In this section we present a cross- simulation framework to evaluate the performance of the cross- framework for WiBro or e OFDMA systems, presented earlier. We need to simplify the operation of the for efficient cross- analysis of e performance, because we cannot implement all operations of the and s due to practical problems, such as simulation time and simulator complexity, when cross- operation is simulated. So we introduce the simulation environment considered in our simulator for cross- analysis, how to apply simulation results to a simulator, and how the interworks with the or upper. We use the MATLAB tool for simulation, and OPNET as an event-driven simulator for cross- analysis of the and s. SIMULATION ENVIRONMENT In order to model a physical channel for cross analysis between e and s, we consider path loss, log-normal shadowing, and frequency-selective Rayleigh fading, according to each user s mobility [12]. We consider an e OFDMA system that uses 9 MHz system bandwidth with 1024 FFT size and 5 ms TDD frame structure. This e OFDMA system supports mobility and a multicell environment. Seven hexagonal cells for this multicell environment are considered, and each cell experiences interference from the first- and second-tier cells. Mobility affects link-level performance such as packet error rate (PER), and is divided into stationary, pedestrian, and vehicular according to user velocity. Pedestrians can move in arbitrary directions at velocities from 1 to 10 km/h, while vehicles can move in only four IEEE Communications Magazine December

7 Because the simulation of many cells requires too long a simulation time and great computing power, we implement only seven central cells in reality and model the remaining 30 cells virtually by the seven central cells, through wrap-around cell configuration. directions at velocities of km/h. Here, each user s direction and velocity changes periodically during the total simulation time. This e system supports five QoS scheduling types: unsolicited grant service (UGS) for constant bit rate service, real-time polling service (rtps) for variable bit rate service, extended real-time polling service (ertps) for voice over IP (VoIP) service with silence suppression, non-real-time polling service (nrtps) for non-real-time variable bit rate, and best effort (BE) for service with no rate or delay requirements. We use the following traffic models: VoIP using a Markov source model with activity factor (full rate: 9.6 kb/s), video streaming with a source video rate of 32 kb/s and 10 frames/s, FTP with exponentially distributed reading time (mean: 180 s) and truncated-lognormally distributed file size (mean: 2 Mbytes), and HTTP divided into ON/OFF periods representing Web page downloads and the intermediate reading times [13]. VoIP traffic is supported by the UGS, rtps, or ertps scheduling type, while video streaming, FTP, and HTTP are supported by the rtps, nrtps, and BE scheduling types, respectively. SIMULATOR DESIGN TO EFFECTIVELY APPLY SIMULATION RESULTS TO A SIMULATOR As mentioned above, we need to simplify operations to simulate cross- operation between and s, and so describe how to apply simulation results, such as a SINR-PER table to a simulator. In order to model the first- and second-tier cells for a hexagonal cell structure, 37 cells are needed in total. Because the simulation of many cells requires too long a simulation time and great computing power, we implement only seven central cells in reality and model the remaining 30 cells virtually by the seven central cells, through wraparound cell configuration. The e OFDMA system supports both distributed subcarrier permutation mode and adjacent subcarrier permutation mode. We present a simple method for calculating the SINR for each mode. We assume that one burst, which is transmitted by one serving BS and experiences intercell interference from the first- and secondtier cells, occupies N b,i subchannels in the ith symbol, and that one BS can maximally use N subchannels for DL burst transmission. For distributed subcarrier permutation mode, since the effect of intercell interference averaging is obtained, the DL SINR for one burst can simply be modeled as i Pi ( Nb, i / Ni) SINRDL, dist =, (3) j i Pj, i ( Nb, i / N) +η b where N i denotes the number of subchannels that the serving BS uses for DL burst transmission in the ith symbol, and η b is the background noise power that the corresponding burst experiences. However, j denotes one of the indices of neighbor BSs that are responsible for the firstand second-tier cells that affect the serving cell. Thus, P i and P j,i denote the received power from the serving BS and the neighbor BSs in the ith symbol, respectively. By contrast, in adjacent subcarrier permutation mode, the serving BS examines whether or not the neighbor BSs use the same subchannels as they use for DL burst transmission. If so, power received on these subchannels is directly added to the interference power. The UL SINR can be modeled in the same way as the DL SINR. The error of non-harq bursts can be modeled simply by referring to the SINR-PER mapping table according to mobility and MCS level, which is prepared in advance through the linklevel simulation in the. However, the error of HARQ-bursts should consider the combined gain of the original transmission and incremental redundancy (IR). We present two methods to reflect this combined gain of the HARQ scheme. Method 1. We prepare all results that can show SINR-PER curves for the original transmission, the first retransmission, the second retransmission, and so on, according to each MCS level and mobile speed, through extensive link-level simulation of the. Method 2. We consider only the approximate combined gain of the current retransmission to the previous (re)transmission. For example, the PER of a burst with the first retransmitted IR is decided by referring to the SINR-PER mapping table for non-harq with SINR org + α 1 when the SINR of the original transmission is SINR org and the combined gain by the first IR is α 1. This method cannot reflect the exact combined gain of HARQ in a simulator, but makes the link-level simulation of the simpler than method 1. Using these design methods for interference modeling, SINR calculation, and burst error, we can analyze effectively the performance of cross protocols, such as a channel-aware scheduler, band AMC, and HARQ, in a simulator. IMPLEMENTATION OF A SIMULATOR Figure 4 shows the architecture of a simulator for the performance analysis of cross- protocols between e OFDMA and s. Upper- protocols are considered simply a link- level traffic generator that generates VoIP, video streaming, FTP, and HTTP traffic. In addition, the CS protocol is responsible for the simple mapping of the service flow identifier (SFID) and CID in a simulator. Upper- protocol data units (PDUs), which are created by the traffic generator, are inserted into queues in the after SFID-CID mapping, and data packets in these queues are treated as service data units (SDUs). These SDUs are fragmented into various sizes according to the scheduling operations, and are processed by a selective repeat ARQ mechanism for ARQ-enabled connections. The scheduler classifies connections into five QoS classes: UGS, rtps, ertps, nrtps, and BE. These QoS classes are associated with certain predefined sets of QoS-related service flow parameters, and the scheduler supports the appropriate data handling mechanisms for data transport according to each QoS 142 IEEE Communications Magazine December 2005

8 Upper Traffic generator Traffic Traffic data data Traffic data CS SFID-CID mapping SDU SDU SDU Control flow Message flow Handover module (backbone communication: BS only) management Fragmentation/defragmentation ARQ (selective repeat) Scheduler (UGS, rtps, ertps, nrtps, BE) Diversity Band AMC scheduler scheduler Fragment Fragment of SDU of of of SDU SDU SDU Non-ARQ queue Fragment Fragment of of SDU SDU Scheduled Block Block Block Block ARQ queue Block Block Ranging DL/ UL- MAP PDU Concatenation/deconcatenation Header Subheader Payload Subheader Header Payload PDU PDU PDU Mobility (MS only) CDMA code HARQ PDU Resource allocation CQI SINR/PER modeling UL-MAP FCH DL-MAP Preamble UL control channel n Figure 4. simulator architecture for cross- analysis. class. First, the UGS is designed to support real-time data streams that generate fixed-rate data such as T1/E1 and VoIP without silence suppression. For UL, this service offers fixedsize grants for data transport on a real-time periodic basis. Second, the rtps is designed to support real-time data streams consisting of variable-sized data packets that are issued at periodic intervals, such as MPEG video. For UL, this service offers periodic unicast request opportunities. Third, the ertps is designed to support real-time data streams that generate variable size data packets on a periodic basis, such as VoIP services with silence suppression. For UL, this service offers a mechanism for periodic UL allocations, which may be used for requesting the bandwidth as well as for data transfer, considering the traffic characteristics of VoIP with silence suppression. Fourth, the nrtps is designed to support delay-tolerant data streams consisting of variable-sized data packets for which a minimum data rate is required, such as FTP. For UL, this service offers unicast polls on a regular basis; typically, in an interval on the order of 1 s or less. Fifth, the BE service is designed to support data streams for which no minimum service level is required and therefore may be handled on a space-available basis. For UL, this service may offer contention request opportunities. The scheduler then classifies users into an AMC group and diversity group according to QoS and channel state, and finally, it applies the scheduling algorithm in either the distributed or adjacent subcarrier permutation mode. The BS can apply variable channel-aware scheduling schemes using the SINR information reported by MSs on UL control channels. If we only consider data services without delay bound, throughput maximizing scheduling algorithms, such as PF or max CIR schemes, can be used. Otherwise, scheduling algorithms that consider QoS as well as cell throughput should be considered; for example, the M-LWDF scheme [4, 9]. However, the scheduler in the adjacent subcarrier permutation mode (or band AMC scheduler) can improve throughput performance considerably by optimally or suboptimally assigning subcarriers to users according to their frequency selectivity [14, 15]. payloads are built by the scheduler, and then PDUs are built by adding the generic header and inserting subheaders such as fragmentation subheader, packing subheader, grant management subheader, and fast feedback subheader, if necessary. PDUs that use the same MCS level can be concatenated and build a burst. Also, HARQ can be applied in the case of HARQ-enabled connections. All the above operations, such as scheduling, decision of MCS level, HARQ, and resource IEEE Communications Magazine December

9 Packet transmission delay (s) DL delay UL UGS delay UL rtps delay UL ertps delay Number of VoIP users rtps n Figure 5. Packet transmission delay of SDUs vs. number of VoIP sessions for UGS, rtps and ertps. UGS 100 allocation, can be optimized by cross- mechanisms using information. DL/UL-MAP is implemented for the delivery of general resource allocation information, and compact DL/UL MAP is implemented for the operation of HARQ and band AMC. Moreover, some management messages are implemented for variable management operations, such as ranging, dynamic session management, ARQ, and handover. A backbone network of BSs is built for handover operation. We can analyze variable cross- performance between and s of such operations as HARQ and band AMC, through this simulator for cross- analysis. In addition, this simulator can be utilized for the efficient design of cross- protocols such as scheduling, resource allocation, and determination of PDU size. 80 ertps PERFORMANCE ANALYSIS AND DISCUSSION In this section we introduce the method of derivation of VoIP capacity through the cross simulator presented earlier, and analyze the VoIP capacity for UGS, rtps, and ertps, respectively. Moreover, we analyze and discuss the capacity enhancement of OFDMA systems through cross- protocol designs, such as channel-aware scheduling schemes and band AMC scheduling. The simulation results were obtained using the following MCS levels: quaternary phase shift keying (QPSK) 1/12, QPSK 1/8, QPSK 1/4, QPSK 1/2, 16-quadrature amplitude modulation (QAM) 1/2, 16-QAM 3/4, 64-QAM 2/3, and 64- QAM 5/6. Low density parity check code (LDPC) was used, and coding rates lower than 1/2, which are needed to solve intercell interference problem of cell boundary users, were obtained by repetition. Users were distributed uniformly in seven cells and experienced interference from the first- and second-tier cells. The ratio of DL to UL subframe length was 2:1, and the simulation environments described previously were considered. Figure 5 shows the packet transmission delay of SDUs of VoIP sessions for UGS, rtps, and ertps, respectively. The DL scheduler uses a round-robin (RR) scheduling scheme, while the UL scheduler uses UGS, rtps, or ertps scheduling types that do not utilize multi-user diversity by fading channel. Here, the DL and UL MCS levels of each user are decided from the CINR value reported by the MSs or measured by the BS. In other words, the DL or UL scheduler in this simulation utilizes physical cross- information such as average CINR. Moreover, the ertps provides efficient UL cross- resource allocation mechanisms that utilize the traffic characteristics of VoIP with silence suppression. Because the level of interference increases with the number of VoIP sessions, and thus transmitters select more robust MCS levels in order to meet the requirement of packet error rate for VoIP service, throughput is saturated, many packets are queued, and thus packet transmission delay rapidly increases with the number of VoIP sessions due to this large queuing delay. Here, we can decide the maximum number of VoIP users from these results, considering the requirement of packet transmission delay for VoIP service on air interface. For example, when its delay requirement is 60 ms, the maximum supportable number of VoIP users is limited by UL rather than DL, and the voice capacity is obtained as 76 VoIP users/cell, when using the UGS scheduling type. In this simulator the UGS scheduler aggregates the VoIP packets within the UGS intergrant time, which may adaptively increase with system load. Then the aggregated packet is transmitted. Thus, this UGS with adaptive intergrant time can support many more VoIP users than can UGS with a fixed intergrant time. However, about 68 and 92 VoIP users/cell can be supported when using rtps and ertps, respectively. Hence, the ertps can increase the VoIP capacity by 21 and 35 percent compared to UGS and rtps, respectively. The ertps offers a scheduling mechanism that builds on the efficiency of both UGS and rtps, by operating like UGS with variable grant size adaptive to the VoIP codec rate during the VoIP ON-period while operating like rtps during the VoIP OFF-period. Figure 6 shows average cell throughput for non-real-time data traffic when using nonopportunistic scheduling such as RR considering only average CINR and channel-aware opportunistic scheduling schemes such as PF scheduling and band AMC scheduling. The PF scheduler utilizes the channel variation in the time domain, while the band AMC scheduler utilizes the channel variation in both frequency domain and time domains. Here, throughput means the total size of PDUs transmitted per second in the. throughput means the total size of SDUs successfully received per second in the. We can calculate the overhead due to retransmission, header or subheaders, and management messages, based on the ratio of throughput to throughput. However, MAP overhead means the 144 IEEE Communications Magazine December 2005

10 Average DL cell throughput (Mb/s) throughput throughput DL diversity only 1) (RR scheduling) DL diversity only 2) (PF scheduling) DL diversity (PF) 2) + band AMC 3) Average UL cell throughput (Mb/s) throughput throughput UL diversity only 1) (RR scheduling) UL diversity only 2) (PF scheduling) UL diversity (PF) 2) + band AMC 3) RR scheduling: Round robin scheduling PF scheduling: Proportional fair scheduling 1) Using average channel quality information 2) Using instantaneous channel quality information in the time domain 3) Using instantaneous channel quality information in the frequency domain as well as the time domain n Figure 6. Average cell throughput of e OFDMA system under various cross- protocol designs: a) DL average cell throughput vs. scheduling type; b) UL average cell throughput vs. scheduling type. ratio of the amount of resources occupied by DL/UL-s to the total amount of used resources. Because these s need to be received successfully by all MSs in a cell, they have to be transmitted using the most robust MCS level; this is one of the most serious problems in e systems. We observed from the simulation result that these s occupy up to percent of DL resources. However, we can improve both DL and UL average cell throughput by about percent, as shown in Fig. 6, due to careful cross- approaches (e.g., PF and band AMC scheduling) that apply the cross- adaptation framework and primitives presented earlier. In addition, we can observe that efficiency is lowest, and throughput is not much improved, when using DL band AMC scheduling. The reason is that this case needs a for each band AMC user, as well as for each burst. These results indicate that the number of band AMC users scheduled in a frame should be decided by considering overhead caused by MAP messages. Many other factors affect system performance, and most of these factors pertain to the design of cross- protocols. For example, the determination of the PDU size is a very important cross- issue. A PDU that is large enough to obtain a forward error correction (FEC) effect by channel coding may be transmitted. However, this FEC effect cannot be utilized sufficiently for low-rate service such as voice with a small packet size. Hence, it would be wise to consider alternative mechanisms to obtain the FEC effect, such as packet aggregation within a delay bound. In addition, the system should consider a multicell environment, so naturally it is important to try to improve the performance of cell-boundary users. In particular, the intercell interference problem of the UL cell boundary is severe. It is possible to solve this problem through frequency reuse, but this method requires very careful cell planning and cannot provide good trunk efficiency. Hence, it may be advisable to search for a smart solution, such as intercell-interference-aware resource allocation, scheduling, power control, and adaptive frequency reuse. Moreover, in practical terms, the performance of band AMC subchannels is not much better than that of diversity subchannels because of the feedback delay of channel quality information and scheduling complexity. Thus, it will be necessary to develop smart band AMC scheduling algorithms. It should be noted that spatial multiplexing using an adaptive antenna system (AAS) is provided in systems. For the improvement of system capacity through AAS, all algorithms such as scheduling, resource allocation, and the HARQ scheme should be redesigned carefully considering MIMO channel information. All the issues mentioned above are very important for improving system performance, and a cross- approach is necessary. In the future we will study the protocol design methodology for these cross- issues mentioned above and analyze its system performance with a cross- simulator. CONCLUSIONS Considering WiBro or e OFDMA systems, we present a cross- adaptation framework for inter operation between the and s, and a design example of primitives to exchange information for cross- protocol operation. A - cross- adaptation scheme for efficient resource allocation classifies users into AMC and diversity user groups according to QoS and channel state, and each user group is scheduled separately. This can improve the average cell throughput by percent. That is, cross- protocol design has a large effect on system performance, and physical information should be utilized very IEEE Communications Magazine December

11 For the improvement of system capacity through AAS, all algorithms such as scheduling, resource allocation and the HARQ scheme should be redesigned by carefully considering MIMO channel information. carefully for efficient cross- operation. In addition, we provide a simulation framework for this cross- analysis between the and s. We have shown that this simulator can be utilized for the efficient design of cross protocols such as scheduling, resource allocation, determination of PDU sizes, and ARQ window size. The design and implementation methods of a simulator based on cross- protocol operation can also be utilized in future systems based on OFDMA. REFERENCES [1] C. Eklund et al., IEEE Standard : A Technical Overview of the WirelessMAN Air Interface for Broadband Wireless Access, IEEE Commun. Mag., June 2002, pp [2] A. Ghosh et al., Broadband Wireless Access with WiMAX/ : Current Performance Bandchmarks and Future Potential, IEEE Commun. Mag., Feb. 2005, pp [3] I. Koffman and V. Roman, Broadband Wireless Access Solutions based on OFDM Access in IEEE , IEEE Commun. Mag., Apr. 2002, pp [4] A. Jalali, R. Padovani, and R. Pankaj, Data Throughput of CDMA-HDR a High Efficiency-high Data Rate Personal Communication Wireless System, Proc. IEEE VTC2000, vol. 3, May 2000, pp [5] S. Shakkottai and T. S. Rappaport, Cross-Layer Design for Wireless Networks, IEEE Commun. Mag., Oct. 2003, pp [6] P. Liu, R. Berry, and M. L. Honig, Delay-Sensitive Packet Scheduling in Wireless Networks, Proc. IEEE WCNC 2003, vol. 3, Mar. 2003, pp [7] G. Song and Y. Li, Adaptive Resource Allocation Based on Utility Optimization in OFDM, Proc. IEEE GLOBE- COM 2003, vol. 2, Dec. 2003, pp [8] J. Rhee, J. M. Holtzman, and D. Kim, Scheduling of Real/Non-Real Time Services: Adaptive EXP/PF Algorithm, Proc. IEEE VTC 2003-Spring, vol. 1, Apr. 2003, pp [9] M. Andrews et al., Providing Quality of Service over a Shared Wireless Link, IEEE Commun. Mag., Feb. 2001, pp [10] S. Kallel, Analysis of a Type-II Hybrid ARQ Scheme with Code Combining, IEEE Trans. Commun., vol. 38, Aug. 1990, pp [11] H. Zheng and H. Viswanathan, Optimizing the ARQ Performance in Downlink Packet Data Systems With Scheduling, IEEE Trans. Commun., vol. 4, Mar. 2005, pp [12] ITU-R Rec. M.1225, Guideline for Evaluation of Radio Transmission Technologies for IMT-2000, [13] C , 1xEV-DV Evaluation Methodology Addendum, July [14] C. Y. Wong et al., Multicarrier OFDM with Adaptive Subcarrier, Bit, and Power Allocation, IEEE JSAC, vol. 17, no. 10, Oct. 1999, pp [15] Z. Shen, J. G. Andrews, and B. L. Evans, Optimal Power Allocation for Multiuser OFDM, Proc. IEEE GLOBECOM 2003, Dec. 2003, pp BIOGRAPHIES TAESOO KWON [S 01] (tskwon80@comis.kaist.ac.kr) received his B.S. and M.S. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, in 2001 and 2003, respectively. He is currently working toward a Ph.D. degree in electrical engineering at KAIST. His research interests include radio resource management and multiple access protocols in wireless communication systems such as WiBro, , 3GPP LTE, and 4G systems and performance analysis of CDMA and OFDM mobile communication systems. SUNGHYUN CHO [S 97, M 05] (drcho@samsung.com) received his B.S., M.S., and Ph.D. in computer science and engineering from Hanyang University, Korea, in 1995, 1997, and 2001, respectively. Since 2001 he has been with Samsung Advanced Institute of Technology, where he has been engaged in the design and standardization of and upper s of B3G, IEEE e, and WiBro systems. His research interests include radio resource management, cross- design, and handoff in wireless systems. HOWON LEE [S 04] (hwlee@comis.kaist.ac.kr) received his B.S. and M.S. degree in electrical engineering from KAIST in 2003 and 2005, respectively. Currently, he is working toward his Ph.D. at KAIST. His current research interests include radio resource management, mobility management, and next-generation mobile communications SIK CHOI [S 05] (schoi@comis.kaist.ac.kr) received his B.S. and M.S. degrees in electrical engineering from KAIST in 2003 and 2005, respectively. Currently, he is working toward his Ph.D. at KAIST. His research interests include mobility management, adaptive modulation/coding, and B3G mobile communications. JUYEOP KIM [S 05] (jykim@comis.kaist.ac.kr) received his B.S. degree in electrical engineering from KAIST in 2004, and is currently working on his Master s at KAIST. His research interests include for multicasting and control overhead minimization in. SANGBOH YUN [M 97] (sbyun@samsung.com) has been a senior research engineer with Samsung Advanced Institute of Technology, Korea, since August From 2000 to July 2001 he was with NeoSolution, Inc. as founder and CEO/CTO. From 1994 to 1999 he was with DAEWOO Telecom, Inc. as a research engineer. He received his B.S. and M.S. from the Department of Electronics Engineering of Korea University, Seoul, in 1994 and 1998, respectively. He is currently a Ph.D. candidate in the Department of Electronics Engineering of the same university. His research interests are wireless communication systems, radio resource management,, and particularly their applicable issues to B3G mobile communication systems. WON-HYOUNG PARK [S 05] (whpark@samsung.com) received B.S. and M.S. degrees in electrical engineering from Seoul National University, Korea, in 1998 and 2000, respectively. He is currently working toward a Ph.D. degree in electrical engineering and computer science at Seoul National University. Since 2000 he has been with Samsung Advanced Institute of Technology, Suwon, Korea. His research interests include radio resource management, cross- design, and quality of service in wireless network. DONG-HO CHO [M 85, SM 00] (dhcho@ee.kaist.ac.kr) received a B.S. degree in electrical engineering from Seoul National University in 1979, and M.S. and Ph.D. degrees in electrical engineering from KAIST in 1981 and 1985, respectively. From 1987 to 1997 he was a professor in the Department of Computer Engineering at Kyunghee University. Since 1998 he has been at KAIST, where he is a professor in the Department of Electrical Engineering and Computer Science. His research interests include wired/wireless communication networks, protocol, and services. KIHO KIM [M 91, SM 03] (kihokim@samsung.com) received a B.S. degree from Hanyang University, Korea, in 1980, an M.S. degree from KAIST in 1982, and a Ph.D. degree from the University of Texas at Austin in 1991, all in electrical engineering. From 1982 to 1987 he was with the Korean Broadcasting System, where he developed the Korea Teletext System. Since 1991 he has been with the Samsung Advanced Institute of Technology, Korea, where he has been engaged in research on HDTV, ADSL, and DVD read channel projects. He is currently vice president of the Communication Laboratory, Samsung Advanced Institute of Technology, and his research interests include mobile and wireless communication and signal processing. 146 IEEE Communications Magazine December 2005

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