Downlink Performance Analysis of Cognitive Radio based Cellular Relay Networks

Transcription

1 Downlink Performance Analysis of Cognitive Radio based Cellular Relay Networks Seungmo Kim, Wan Choi, Yonghoon Choi, Jongmin Lee, Youngnam Han, and Insun Lee School of Engineering, Information and Communications University 13-6, Munji-Dong, Yuseong-gu, Daejeon, , Korea {smkim, wchoi, openmind, jongminlee, Communication & Networking Lab, Samsung Advanced Institute of Technology San14, Nongseo-dong, Giheung-gu, Yongin-si, Gyeonggi-do, , Korea Abstract In this paper, we propose two operating scenarios of employing cognitive radio to downlink of relay networks. The first scenario uses ISM band while the second scenario exploits opportunity of channel use at UHF spectrum. The capacity gain over conventional relay is investigated in terms of the normalized system capacity. We first statistically model the spectrum usage pattern in those bands and derive the achievable data rate and outage probability in order to analyze performance gain in the view of practical system establishment. Then, we define system capacity by normalizing the effect of bandwidth so that a fair comparison of performance is accomplished. Through our analysis and simulations, it is shown that the proposed scenarios are able to achieve significant gains over a conventional cellular relay system in terms of the normalized system capacity. As a result, relay combined with cognitive radio can be considered as a viable solution to get higher downlink capacity. keywords Relay, downlink, cognitive radio, capacity I. INTRODUCTION Relay transmission strategy in cellular systems were originally proposed to cover dead spots, e.g., subways, tunnels or in-building areas, where a base station (BS) cannot appropriately support mobile users [1]. Recent studies have shown that a cellular system adopting relay transmission strategy has other potential advantages such as increased system capacity and performance improvement if relay stations (RSs) cooperate for transmission [2]-[4]. In most radio systems, users rarely utilize all the assigned frequency bands at a certain time and a location. Such a frequency band not occupied by a primary user (PU) is called a spectrum hole [5]. The spectral inefficiency caused by the spectrum holes motivated cognitive radio (CR) technology that present unlicensed secondary users (SUs) an opportunity for using spectrum holes. CR makes an SU to find and use the spectrum hole without interrupting the operation of PU. The fact that CR technique brings more efficient spectrum utilization motivates its adoption to cellular relay networks. The advantage is interference reduction between relay stations and base station by allocating orthogonal frequency bands to relay stations from base stations for downlink. Even though the same advantage can be achieved by using a dedicated channel which is orthogonal to cellular band, it is much more efficient to use orthogonal bands by opportunistic spectrum access in terms of spectral efficiency. Opportunistic spectrum access for wireless communications can be classified into two aspects: access in licensed spectrum and unlicensed spectrum. Cognitive utilization of licensed frequency bands has been investigated in [6]-[1]. IEEE working group was recently formed to develop CR systems for secondary operation in UHF band [6]-[7]. Several statistical models describing competitions between PUs and SUs were developed in [8]-[1]. On the other hand, the usage of unlicensed spectrum have been studied in [11]- [13]. In [11], the authors took a game theoretic view on the spectrum sharing in open bands and proposed a punishment strategy based on the Nash equilibrium of repeated games. A similar game theoretic approach was found in [12]. A random access protocol for fairness access in unlicensed spectrum was proposed and analyzed by continuous time Markov models in [13]. Furthermore, information theoretic analysis of CR systems was presented in [14]-[15]. In [14], ergodic capacity was analyzed based on a two-level switch model. Cognitive radio environment was interpreted as a combination of interference channels and an achievable region for cognitive radio channel was developed in [15]. This paper analyzes downlink capacity of cellular relay systems employing CR in relay transmission and quantifies the capacity gains based on a multiple-cell system architecture. Two specific scenarios of designing cellular relay network using unlicensed ISM band and licensed UHF band are considered. We statistically model the primary users temporal channel usage pattern in those bands as the channel availability factor. Based on the factor, achievable rate, outage probability and finally the normalized capacity are derived. Through our analysis and simulations, it is shown that the proposed scenarios are able to accomplish significant gains over a conventional cellular relay system in terms of the normalized capacity. II. SYSTEM MODEL A. Configuration of CR based cellular relay system We provide a simplistic, 1-dimensional multiple-cell system model as given in Fig. 1. which can be easily extended to a general two-dimensional cellular array model. There exist m cells with n relay stations in each cell. RSs are assumed to be physically connected to BS over dedicated links such

2 Fig. 1. CR based relay system model in multiple-cell environment (m = 3 and n = 2) as fiber optic lines. The relay stations can be considered to have coverage radius L r and be located at the distance d r from the corresponding BSs. L r depends upon carrier frequency used by the RS. A relay station can make the perfect scanning/sensing of ISM or UHF channels in order to find spectrum holes and utilize them for the downlink. In this way, a relay station and mobile users supported by the RS construct a cognitive radio system in which transmitter sends information to receivers by opportunistically using spectrum holes. The RS becomes a cognitive (or secondary) transmitter and each mobile corresponds to a cognitive receiver. Users in the system can make a selection diversity between downlink of BS and that of RS. B. Multiple access of relay stations for downlink It should be noted that downlink of relay stations in the proposed system model serve over orthogonal downlink channel to downlink of base stations. It leads that downlink of the RSs are not interfered by either base stations in home cell or in other cells. In addition, downlink of any competing cognitive relay stations in other cells do not occur interference, too. It is possible with the assumption that relay stations cannot identify the type of device in the CR channel - primary systems or other secondary devices such as relay stations in other cells, but they only know whether the channel is occupied or not. In other words, as long as an RS uses a spectrum hole, then no other RS can serve over the same channel. Cognitive relay stations never share a spectrum hole so that interference between them does not occur. In this way, competition between secondary devices in the system can be included in the statistical modeling of channel usage which is given in the following Section III. Note also that downlink channels of BSs suffer from interference from downlink of adjacent BSs depending on the frequency reuse factor τ while relay stations are not associated with intracell and inter-cell interference. III. UTILIZATION OF SPECTRUM HOLES IN ISM AND UHF BANDS BY COGNITIVE RADIO For both ISM and UHF band, it is assumed that a channel is occupied by only one primary user at a time. In this way, the channel usage of different primary users is supposed to be uncorrelated. A. Scenario I: Utilization of spectrum holes in ISM band In ISM band, a relay station can use a channel even though there exists a primary user, whenever interference caused by channel usage of the RS is kept below a tolerable level. The reason is that the ISM band is an unlicensed band, which is open to any radio system under a certain interference limit. So the channel utilization in ISM band turns to be a simple multiple access with the assumption that every device in the band perfectly keeps the power constraint. Hence, the channel utilization can be modeled similarly to Erlang B model which has been furnished in [16]. Parameters used to identify users access to the channel are λ and 1/µ, which represent arrival rate and service time of each user, respectively. The probability that k out of N r channels in ISM band are occupied can be given from [16] by P ISM k = (λ/µ)k/ k!. (1) N r (λ/µ) i/ i! i= The channel availability factor, which is defined as the ratio of average number of vacant channels over the total number of channels in ISM band, can be obtained from (1) as α = 1 P N ISM ISM r 1 +2 PN + + N r 2 r P ISM (2) N r where <α<1. The closer α to 1, the higher the possibility that a cognitive device (a relay station in this paper) can utilize unused spectrum. B. Scenario II: Utilization of spectrum holes in UHF band Unlike the previous scenario, we see that a cognitive system must be transparent to the corresponding primary user. So in our case, relay stations must stop transmission and search another spectrum hole whenever the PU claims to use the channel. Suppose that a cognitive system can search spectrum holes over N r channels. In [9], the channel usage pattern of primary users is assumed to be i.i.d. ON/OFF random process. It makes their spectrum utilization not to be correlated between each channel as assumed before. Based on the ON/OFF model, the

3 TABLE I THE CHANNEL AVAILABILITY FACTOR OF ISM AND UHF BAND ACCORDING TO THE TRAFFIC LOAD OF PRIMARY USERS Type of band ISM band UHF band N r 79 6 B r 1MHz 6MHz Traffic load of PU λ/µ =5 λ/µ =3 β i =.1 β i =.5 α probability that the ith channel is occupied is defined in [9] as Ton i β i = Ton i + Toff i (3) where Ton i and Toff i indicate the mean time that the PU is turned on and off in the ith channel, respectively. Then the probability that an RS of our system finds k vacant channels in UHF band is [9] P UHF k = ( ) Nr k c=1 [ Π (1 β i ) Π β j ] (4) i Sc k j {1,2,,N r} Sc k where Sc k represents the cth set, consisting of k out of N r N channels, which is one of r! k!(n r k)! sets. It can be formed as S1 k = {1, 2,,k}, S2 k = {2, 3,,k+1}, and so on. We extend (3) and (4) to develop the channel availability α in the same manner with ISM scenario as α = 1 P 1 UHF +2 P2 UHF + + N r PN UHF r. (5) N r C. Comparison of channel availability N r are B r denote the total number of channels in ISM or UHF band and bandwidth per channel, respectively. According to [17] and [18], wireless communication systems implemented in 2.4GHz ISM band must operate within GHz and 79 channels, each of which spans 1MHz, are actually usable. We set B r = 1MHz and N r = 79. The Erlang traffic parameter of users in ISM band, λ/µ, is supposed to be 5 or 3 as an example of light or heavy traffic in the simulation and comparison. Table I shows the channel availability factor according to λ/µ, the parameter of traffic load in Erlang B model. Specifically, α =74/79.94 and α =49/79.62 for λ/µ of 5 and 3, respectively. On the other hand, secondary devices trying to communicate over UHF band are mandated to operate within MHz by IEEE Even though the standard regulates the bandwidth of each channel to be 6 8MHz, it can be expanded to 1 8MHz by using fraction of bandwidth adaptively [2]. The maximum number of channels that are allowed to be used by a secondary device is 3, but it may be enlarged as well by mobilizing fractions of bandwidth. According to [7], TV stations in US operate from channels 2 to 69 which of all these channels is 6 MHz wide. Therefore, for computing the number of unoccupied channels in the UHF band, we settle the bandwidth of a channel to be 6MHz and the total number of channels to be 6. The channel availability factor α is computed when β i is.1 and.5. Fig. I shows that α =56/6.93 for β i =.1 and α =3/6.5 for β i =.5 is obtained, respectively. IV. DOWNLINK CAPACITY OF COGNITIVE RADIO BASED CELLULAR RELAY NETWORKS In this section, achievable data rate and outage probability is preliminarily derived as measures of system performance based on the channel usage modeling of Section III. A comparison of performance in the view of practical system design is possible with those metrics, but it is needed to normalize the effect of bandwidth in order to fairly prove the technical superiority of CR based cellular relay systems. Therefore, we define the normalized system capacity in this section. Derivation of the normalized system capacity begins from considering cellular environments such as path loss, antenna gains and log-normal shadowing. The received signal power by a mobile user from a transmitter (either BS or RS) expressed in db unit is given by ( ) l d P R = P T + G R + G T PL(d ) 1 log L (6) d where P T, l, d, and d represent the transmit power, the path loss exponent, the distance from the transmitter to the user, and close-in distance, respectively, and L = e ξ is a log-normal random variable with ξ following ξ N[m ξ,σξ 2]. G R and G T denote the transmitter and receiver antenna gain, respectively. Analysis of system capacity for the CR based cellular relay network originates from (6). Note that, due to differences of environment, derivation of the normalized system capacity should be distinguished according to whether a user is served by BS or RS. A. Outage probability of a user in the area of base station Received SINR of a user from home base station at the distance d c is denoted by γ c and derived from (6) as where λ l g c 4πd c γ c (d, τ) = I c + N (7) th P LP b I c = 2 λ g ci 4πd ci (8) i=1 and τ is the frequency reuse factor while P b denotes transmit power of base stations. The channel gain between home BS and the mobile is denoted by g c in (7), while g ci of (8) is channel gain of interference source in ith adjacent cell. Moreover, N th is thermal noise, λ is related to carrier frequency by λ = c/f, and P L represents the path loss reference which is determined according to d, respectively. With (7), the instantaneous data rate (in bps unit) achieved by a user is derived as R c (d, τ) = τn cb c K c log(1 + γ c (d, τ)) (9)

4 where N c and B c are the number of channels that a BS can use and bandwidth of a channel, respectively. K c denotes the number of users sharing the downlink bandwidth of home base station. With the assumption that competing users are assumed to be uniformly distributed, the outage probability of home BS is defined in terms that how many users not to satisfy the threshold R by home BS. It is formulated as P out c = K c 2L c L c E g [Pr[R c (x, τ) <R ]] dx. (1) where E g [ ] denotes the expectation over link gains, which averages the fading effects, and R represents the threshold level of data rate. B. Outage probability of a user in the area of relay station Interference that downlink of relay stations experience is different according to type of cellular relay system in which they are located - cognitive radio based relay or conventional system. 1) CR based relay: The received SINR of a user from a relay station is given by λ l g r 4π(d d PL r) P r γ r (d) = (11) N th where RS transmit power is denoted by P r and the channel gain from RS to the user is represented by g r. In this case, downlink of a relay station does not be interfered from relay stations in other cell because RSs do not share a spectrum hole according to multiple access strategy of Section II. Furthermore, downlink of the RS does not interfere with base stations either in home cell or in other cells since it operates on a channel that is orthogonal to downlink of those BSs by owing to CR. The achievable data rate derived from (11) is then given by R r (d, α) = αn rb r log(1 + γ cr (d)). (12) K r where K r denotes the number of users sharing the downlink bandwidth of the cognitive relay station. Based on (12) the outage probability for the cognitive RS can be derived as below: P out cr = K r 2L r L r E g [Pr[R cr (x, α) <R ]] dx (13). 2) Conventional relay: The received SINR in conventional relay systems is affected by interference from adjacent BSs and hence expressed as λ g r 4π(d d r) l γ r,conv (d, τ) = I r + N (14) th P LP r where I r,conv = 2 λ 4 g ci 4πd ci + λ g rj 4πd rj (15) i= j=1 where g ci and g rj in (14) and (15) indicates the channel gain of interfering base stations and relay stations, respectively. Compare (14) with (11) and note that downlink of a conventional RS interferes with that of all BSs in home cell and adjacent cells as well as all RSs in other cells while a CR based cellular RS does not have interference for downlink. With (14), the achievable data rate by a user is derived by R r,conv (d, τ, β) = τn cb c log(1 + γ r,conv (d, τ)) (16) K r,conv where <β<1 represents the portion which is allocated to users in conventional relay area out of the whole cellular bandwidth. Then outage probability of a conventional cellular relay system is obtained by P out r,conv = K r,conv 2L r L r E g [Pr[R r,conv (x, τ) <R ]] dx (17) where E g [ ] denotes the expectation over link gains, which averages the effects of log-normal shadowing. C. The normalized system capacity Based on the channel usage model of Section III, achievable rate and outage probability of a user in the cellular relay system is derived. They are reasonable metrics of system performance in the view of practical system establishment, but it is needed to normalize the effect of bandwidth for fair and technical analysis of CR based cellular relay systems. Hence, we define the normalized system capacity which aggregates data rates of actually supportable users with a given outage threshold and normalizes it with bandwidth used by the cellular relay system. Users with their achievable data rate satisfying R > R are actually allocated the system bandwidth, so they only are supported by the cellular relay system. Let K r and K c denote the number of users served by a BS and an RS, respectively. Then the normalized capacity is expressed by aggregated data rates achieved by K r +K c users and then normalizing it by the system bandwidth. The normalized system capacity of a CR based cellular relay network is formulated as 1 K c K r C r = Rc i + R j r (18) αn r B r + τn c B c i=1 j=1 where Rr i and Rc j are obtained by (12) and (9), respectively. Note from (18) that aggregation of achieved rates is normalized with sum of cellular bandwidth τn c B c and the bandwidth of spectrum holes αn r B r. In other words, additional bandwidth is utilized based on CR technique. The effect to cellular relay system due to luxury of bandwidth is normalized in definition of the system capacity for fair comparison between relay strategies. On the other hand, the

5 TABLE II SYSTEM PARAMETERS SETUP BS power (P t) 46 dbm RS power (P r) 3 dbm The frequency reuse factor (τ) 3 Path loss exponent (l) 4 Close-in distance (d ) 1m Log-normal fading spread (σξ 2) 8dB Cell radius (L c) 15 m Threshold of data rate (R ) 3 kbps Carrier frequency of downlink of BS 1.8 GHz Carrier frequency of downlink of RS in Scenario I 2.4 GHz Carrier frequency of downlink of RS in Scenario II 7 MHz Outage probability Fig Scenario I (simulation) Scenario I (analysis) Scenario II (simulation) Scenario II (analysis) Conventional relay (simulation) Conventional relay (analysis) λ/µ = 3 λ/µ = 5 β i =.1 β i = The number of users Outage probabilities versus the number of users (R = 3 kbps) normalized system capacity of a conventional cellular relay system is obtained by 1 K c K r C r,conv = Rc i + R j r,conv (19) τn c B c i=1 j=1 where Rr,conv i indicates (16) while Rc j is from (9). In this case, all K r +K c users are commonly normalized by the cellular bandwidth τn c B c since they share the cellular bandwidth without additional bandwidth allocation. V. SIMULATION RESULTS In this section, we compare the performance of ISM and UHF CR based relay network in terms of the normalized system capacity. Table II shows a list of the system parameters used in the simulations. The distance from base station to a relay station, d r,of conventional relay system is preliminarily settled to 12m. For scenario I and II, d r for both relay stations using ISM and UHF band are set to make signal strengths equal to signal strength of conventional one at the cell edge. In this way, the location of a CR based RS is determined by its coverage. UHF based relay stations in scenario II utilize 7MHz carrier frequency for downlink while ISM based relay and conventional relay use 2.4GHz and 1.8GHz, respectively. Hence, in the view of path loss, the larger coverage is obtained with the lower carrier frequency of downlink channel. Two CR based relay scenarios provide relay stations with additional bandwidth for downlink by owing to CR. It makes possible that the whole cellular relay systems acquire he abundance of bandwidth and every relay station serves over the orthogonal downlink channel from downlink of base stations. The proposed scenarios are shown to have lower outage probability due to luxury of bandwidth, which is a valid comparison in the view of practical system establishment. However, it is needed to normalize the effect of radio resource such as bandwidth in order to fairly prove the technical superiority of CR based cellular relay systems. Therefore, we compare system performance of the two proposed relay scenarios over conventional system in terms of the normalized capacity. A. Outage probability Fig. 2 shows outage probabilities versus the number of users. Threshold data rate R is set to 3 kbps. Simulation results are obtained from Monte-Carlo runs and they are compared with numerical results from analysis. As shown in Fig. 2, the outage probabilities of CR based relay systems increase with larger traffic loads in the systems. With heavy traffic load of primary systems, downlink bandwidth that a relay station can serve shrinks. It leads that data rates achieved by users from CR based RS become lower, which consequently causes high outage probability. Besides, Scenario II is shown to have the lowest outage probability, in other words, the largest number of users can be served with a certain outage probability. That is a result of an abundance of bandwidth and the largest coverage of UHF based relay stations. B. The normalized system capacity Table III summarizes the maximum number of supportable users with 1% of outage probability and the normalized system capacity according to traffic load of PU. The normalized capacity is shown to have only little effect from the primary users traffic load. The reason is that the capacity is normalized with respect to bandwidth which is influenced by channel usage of primary users. The improvement of the system capacity in proposed scenarios compared to conventional relay is due to interference reduction. BS and RS establish orthogonal downlink channel canceling interference by using CR. Also, relay stations are not interfered from other cognitive RSs as long as they can never share a spectrum hole. Furthermore, the UHF CR based relay system can cover much wider region without outage so that it can achieve larger aggregated data rates. Hence, Scenario II outperforms other relay systems in both terms of the number of supported users and the normalized system capacity.

6 TABLE III THE NORMALIZED SYSTEM CAPACITY OF RELAY SCENARIOS Type of system Conventional Scenario I Scenario II Traffic load of - λ/µ=5 λ/µ=3 β i =.1 β i =.5 primary systems The maximum number of supported users The normalized system capacity The normalized capacity (bps/hz) Scenario I Scenario II Conventional relay Fig. 3 shows the normalized capacity versus path loss exponent. As path loss exponent increases, the normalized system capacity declines since larger number of outages occur. The numerator of (18), sum of data rates, decreases with greater path loss exponent because coverage of BSs and RSs shrinks while the denominator, system bandwidth, remains constant with given traffic loads. Hence, the normalized capacities of two CR based relay scenarios drop as path loss exponent increases. Further, scenario II shows the greatest capacity as it supports the largest number of users with the largest coverage of UHF based relay stations. VI. CONCLUSIONS In this paper, we analyze the system capacity of downlink of cognitive radio based cellular relay systems. Two scenarios of cellular relay systems are proposed based on cognitive radio technology which utilizes ISM and UHF band opportunistically. We derive achievable rate and outage probability in order to measure system performance in practical perspective. Then, we define the normalized system capacity for those scenarios in order to fairly compare system performance and evaluate performance gain. Our analytical and simulation results reveal that ISM and UHF based relay systems effectively improve the normalized capacity as well as outage probability. These results indicate that adopting CR can be a breakthrough to achieve a performance improvement in downlink of cellular relay networks by reducing inter-cell interference. The developed analytical framework can be employed in analyzing other CR based cellular networks. ACKNOWLEDGMENT This work was supported by Samsung Advanced Institute of Technologies (SAIT). REFERENCES [1] A. A. M. Saleh, A. J. Rustako, and R. S. Roman, Distributed antennas for indoor radio communications, IEEE Trans. Commun., vol. 35, pp , Dec [2] W. Choi and J. G. Andrews, Downlink performance and capacity analysis of distributed antenna systems in a multicell environment, IEEE Trans. Wireless Commun., vol. 6, no. 1, pp , Jan. 27. [3] H. Zhuang, L. Dai, L. Xiao, and Y. Yao, Spectral efficiency of distributed antenna systems with random antenna layout, IEEE Electron. Lett., vol. 39, no. 6, pp , Mar Path loss exponent Fig. 3. The normalized capacities versus path loss exponent (Simulation results only, R = 3 kbps, λ/µ=3, and β i =.5) [4] L. Dai, S. Zho, and Y. Yao, Capacity analysis in CDMA distributed antenna systems, IEEE Trans. Wireless Commun., vol. 4, no. 6, pp , Nov. 26. [5] Simon Haykin, Cognitive radio: Brain-empowered wireless communications, IEEE J. Select. Areas Commun., vol. 23, no. 2, February, 25. [6] IEEE Working Group on Wireless Regional Area Networks, [7] C. Cordeiro, K. Challapali, and D. Birru, IEEE 82.22: An introduction to the first wireless standard based on cognitive radios, J. commun., vol. 1, no. 1, April 26. [8] S. Zhu, L. Shen, and T. P. Yum, Analysis of cognitive radio spectrum access with optimal channel reservation, IEEE commun. lett., vol. 11, no. 4, April 27. [9] C. T. Chou, H. Kim, S. S. N, and K. G. Shin, What and how much to gain from spectrum agility, IEEE J. Select. Areas Commun., vol. 25, no. 3, April 27. [1] B. Wang, Z. Ji, K. J. R. Liu, Primary-prioritized Markov approach for dynamic spectrum access, in Proc. of IEEE DySPAN 27, pp , Dublin, Ireland, April 27. [11] Raul Etkin, Abhay Parekh, and David Tse, Spectrum sharing for unlicensed bands, IEEE J. Select. Areas Commun., vol. 25, no. 3, April 27. [12] D. P. Satapathy and J. M. Peha, Etiquette modification for unlicensed spectrum: approach and impact, in Proc. 48th Annu. Int. IEEE Veh. Technol. Conf., vol. 1, May 1998, pp [13] Y. Xing, R. Chandramoli, S. Mangold, and S. Shankar N, Dynamic spectrum access in open spectrum wireless networks, IEEE J. Select. Areas Commun., vol. 24, no. 3, March 26. [14] S. A. Jafar, and S. Srinivasa, Capacity limits of cognitive radio with distributed and dynamic spectral activity, IEEE J. Select. Areas Commun., vol. 25, no. 3, April 27. [15] N. Devroye, P. Mitran, and V. Tarokh, Achievable rates in cognitive radio channels, IEEE Trans. Inform. Theory, vol. 52, no. 5, pp , May 26. [16] A. Viterbi, CDMA: Principles of spread spectrum communication, Addison Wesley, MA: [17] ANSI/IEEE Std 82.11, IEEE Standard for Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, pp , [18] FCC, 47CFR, Part 18, p.943, Industrial, Scientific and Medical (ISM) Equipment, 27. [19] A. Ghasemi and E. S. Sousa, Fundamental limits of spectrum-sharing in fading environments, IEEE Trans. Wireless Commun., vol. 6, pp , February 27. [2] Doc.: IEEE /5r5, A PHY/MAC proposal for IEEE WRAN systems.