A New Detection and Decoding Technique for 2 N r MIMO Communication Systems

Transcription

1 International Journal on Recent and Innovation Trends in Computing and Communication ISSN: Volume: 6 Issue: A New Detection and Decoding Technique for 2 N r MIMO Communication Systems Karim Hamidian Electrical Engineering Department, California State University Fullerton, CA, USA hamidian@fullerton.edu Wurod Qasim Department of Electronic Engineering, Universityof Diyala Diyala, Iraq Wurod89@csu.fullerton.edu Abstract The requirements of fifth generation new radio (5G- NR) access networs are very high capacity and ultra-reliability. In this paper, we proposed a V-BLAST2 N r MIMO system that is analyzed, improved, and expected to achieve both very high throughput and ultrareliability simultaneously.a new detection technique called parallel detection algorithm is proposed. The performance of the proposed algorithm compared with existing linear detection algorithms. It was seen that the proposed technique increases the speed of signal transmission and prevents error propagation which may be present in serial decoding techniques. The new algorithm reduces the bit error probability and increases the capacity simultaneouslywithout using a standard STC technique. However, it was seen that the BER of systems using the proposed algorithm is slightly higher than a similar system using only STC technique. Simulation results show the advantages of using the proposed technique. Keywords component; MIMO, Parallel Detection and Decoding, V-BLAST. ***** I. INTRODUCTION The major challenge of for 4G- LTE and 5G New Radio (NR) is to maximize both the reliability and data rate of Multiple Input Multiple Output (MIMO) techniques simultaneously [9] [2]. The goal of any MIMO systems is either to combat or exploit the multipath propagations between multiple transmit and receive antennas through two separate techniques, which are called spatial diversity and spatial multiplexing. Table summarizes the MIMO categories [4]. MIMO technique diversity multiplexing Table. MIMO Categories Purpose Approach Method Improve reliability Increase capacity Combat multipath Exploit multipath Space time coding de-multiplexing diversity is a technique thatcombats fading in the multipath channel and improves the reliability of the transmission through transmitting multiple replicas of the same signal by using space time coding (STC) [3]. There are two common metrics that characterize the amount of spatial diversity, which are diversity order and diversity gain [4]. Diversity order represents the number of independent replicas of the same transmitted signal that are available at the receiver. In an N t N r MIMO system, there are N t N r multipath propagation paths between the N t transmit antennas and the N r receive antennas. The general property of the MIMO communications system that holds for many modulation types in Rayleigh fading channel is that the diversity order (N d ) equals to the diversity gain (G d ), and we say the system achieves full diversity if N d = G d = N t N r expression is true. However; spatial multiplexing (SM) technique exploits multipath propagation to transmit the information at a rate up to channel capacity (channel capacity improvements) without increasing the required bandwidth by using the spatial demultiplexing method. In this paper, we propose a novel parallel detection and decoding algorithm for MIMO communication systems. Our algorithmoptimizes the capacity, improves the reliability of transmission without employing a standard STC technique, and prevents any possible error propagation that may be presented in other serial detection and decoding algorithms. The proposed method is compared with current methods that use STC and SM techniques. In this article, it is assumed the channel is Rayleigh flat fading and that channel state information is available at the receiver.for the purpose of presenting the benefits offered bythe proposed model, we consider MIMO systems having two transmitting antennas and different number ofreceiving antenna, withan N r 2. The rest of this paper is organized as follows. Section II provides the transmitter model and its related expressions. Section III shows the proposed detection and decoding algorithm. Simulation results are provided and discussed in section IV. Finally, we conclude in section V. IJRITCC July 208, II. TRANSMITTER MODEL To enhance the performance of MIMO communications in Rayleigh fading environment, a new detection technique is proposed that will exploit the multipath propagation to detect and decode N t received symbols simultaneously and independently.in this paper, it is assumed,n t = 2, and a large number ofantenna elements (antenna array) are used at the receiver. 09

2 International Journal on Recent and Innovation Trends in Computing and Communication ISSN: Volume: 6 Issue: The data stream after channel encoder[6] splits into two data streams, each of which is separately modulated and transmitted by its respective antenna using V-BLAST encoding architecture as shown in fig. [4]. r Nr = ρ Nr,S + Nr,2S 2 + Z Nr 5 The Front End Receiver combines all these received signal components and produce the received signal R in vectorial formas shown in (3). Figure. MIMO Communication Transmitter Using V-BLAST. As fig. shows, the system transmit two modulated symbols at one symbol period. Therefore, the transmitted signal matrix S is S = S S 2 We assume the Rayleigh fading channel state informationis nown at the receiver. Therefore the channel matrix is H =,,2 2, 2,2 Nr, Nr,2 (2) In vector and matrix form, the received signal matrix in the Rayleigh flat fading environments is expressed as [2] and [4]. R = ρ,,2 2, 2,2 Nr, Nr,2 S S 2 + Z Z 2 Z Nr 3 Where Z i is the noise term that is received with the i t receive antenna. III. PROPOSED DETECTION AND DECODING TECHNIQUE Figure 2 shows the receiver model of the proposed novel parallel detection and decoding technique [6] [7], where the two received symbols will be detected and decoded independently and simultaneously. In addition, with the new model, replicas for each transmitted symbol are extracted as the following procedure shows. First, we decompose the channel matrix into two components as follows [4],j H = H H 2, were H j = 2,j (4) Nr j Where in the expression of i,j the first index represents the receiving antenna number and the second index represents the transmit antenna number. From figure 2, the received signal components are r = ρ, S +,2 S 2 + Z, r 2 = ρ 2, S + 2,2 S 2 + Z 2, and Figure 2. Proposed Detector and Decoder for MIMO Communication. Using the proposed algorithm, we can simultaneously and independently extract the two transmitted symbols S and S 2 bypre-multiplying the received signal matrix R by A and A 2 vectors, respectively.decoding of the S j transmitted symbol is obtained by first generating the estimated received signals R j is as follows, j =,2 R j A j R 6 Where A j( Nr is the null space vector for the channel ) component that constitutes the interference for the j th transmitter. Thus, as shown in (7),A is the null space of H 2.. Similarly, A 2 is the null space of H A = nullspace H 2 7 Thus, each of these estimated received signals can be rewritten as follows (eff R j = ρh ) j S j + Z j (8) Where the effective channel matrix represents eff H j = A j H j 9 The noise term is Z j = A j Z i (0) It is interested to note (8) indicates that the energy of R j is only from the S j symbol. This demonstrates that the interferences from the remaining transmitted symbolis suppressed. In addition, there are N r replicas of S j symbol available at the receiver. This implies the receiver achieves a spatial diversity without using standard STC. We can decode S j symbol by applyingmld to (8),which is computed as follows, where S j refers to the estimate ofs j. eff 2 S j = arg min S j R j ρh j S j () F In summary, the transmitter sends two modulated symbols at one symbol period using spatial multiplexing technique, and the receiver is enabled to detect and decode these symbols simultaneously and independently by using the proposedparallel processing algorithm as outlined above. This implies that the speed of signal processing and thus the IJRITCC July 208, 0

3 International Journal on Recent and Innovation Trends in Computing and Communication ISSN: Volume: 6 Issue: throughput will be increased. In addition, at any given symbol period, this algorithm enables the receiver to have accessto twice of (N r N t + ) replicas of each transmitted symbol. This implies that the reliability of this system will be improved also. Thus, it is important to note that this system achieves both spatial diversity and spatial multiplexing simultaneously without employing a standard STC. However; MGSTCs method also achieves both spatial diversity and spatial multiplexing simultaneously, but there are three major differences between two methods. First, MGSTCs method requires that number of transmit antenna to satisfy N t 4,while the new algorithm requires that N t 2. The second improvement is the speed of signal processing at the receiver. In standard MGSTCs method, the decoding of the component codes are performed iteratively and in a serial way. While, using the new algorithm, the decoding of component codes are performed simultaneously and in a parallel way. Also, the proposed parallel processing technique improves the probability of bit error by preventing possible error propagation. In standard MGSTCs method, the decoding of current component code symbol depends on the values of all previously decoded component codes. This implies that if an error occurs in decoding of a component code symbol, such error will propagate to all remaining component codes. This problem does not exist in the proposed decoding algorithm since the receiver decodes the symbols independently and simultaneously. In general, the diversity order a MIMO system depends on the difference between the numbers of receive and transmit antennas. This difference also affects the gain of the replicas of each symbol at the receiver. Thus, the diversity order depends on the number of the rows of the pre-multiplying matrixa j, which is equal to N r N t +. Thus, the diversity order (N d ) of a MIMO communication system using the proposed parallel decoding technique is: N d = 2 N r N t + (2) Also, for (2 N r ) MIMO communication systems, the channel capacity is given by the expression shown in (3) when ran of channel matrix isr = 2 [2], [3] and[4]. C 2 N r MIMO = 2 i= log 2 + ρ 2 λ i 3 Where λ i is the eigenvalue of the channel matrix. IV. SIMULATION RESULTS [6] In this section, we assume that N t = 2 and N r = 2, 3, and 4 respectively, and that the transmitter employs spatial multiplexing technique. The receiver can extract two transmitted symbols independently and simultaneously by using the new detection algorithm. Figure 3 shows the performance results of the proposed method for a (2 2)MIMO communicationsystem compared with two separate systems, one is a 2 2 MIMO usingalamouti code that can achieve only spatial diversity, and another is a 2 2 MIMO with SM that uses serial decoding and it can achieve only spatial multiplexing. In figure 3 the bit error probability is plotted versus E b /N 0 in db in Rayleigh fadingchannel and using BPSK modulation for all three different systems. Our proposed decoding method is unique, because currently there is no a 2 2 MIMOsystem that can achieve both spatial diversity and spatial multiplexing simultaneously. Figure 3. BER of different types of 2 2 MIMO systems. The bit error probability expression of a parallel decoding system is P b 2 2 MIMO Parallel decoding = 2 μ μ (4) Where μ = IJRITCC July 208, ρ +ρ. Where ρ refers to the time averaged signal to noise ratio. It nown that the diversity order is equal to the slope of the bit error probability curve in the linear region [4]. Thus, the diversity order of this system approaches ton d = 2. The above results show the system that uses thenew decoding algorithm achieves significantly lower bit errors rate (BER) than the system that uses spatial multiplexing technique only, but the new model has higher bit errors rate than the system that uses Alamouti code technique only. On the other hand, the proposed parallel processing algorithm improves the channel capacity as shown in Fig.4. The figure shows that the channel capacity of the proposed system is equal to the channel capacity of the 2 2 MIMO systemthat uses spatial multiplexing technique, and it is twice of the channel capacity of the2 2 MIMO systemthat usesalamouti code. The following figures show that increasing the number of the receiving antennas leads to more performanceimprovements in MIMO communications. Fig.5 shows the performance results of a (2 3) MIMO communication system using the proposed model compared with a 2 3 MIMO using Alamouti code and a 2 3 MIMO using spatial multiplexing and serial decoding. The theoretical expression of the bit error probability for these methods is P b 2 3 MIMO Parallel decoding = 4 2 μ 3 = μ The bit error probability curvein figure 5 showsthat the diversity order of this system approaches ton d = 4. 5

4 International Journal on Recent and Innovation Trends in Computing and Communication ISSN: Volume: 6 Issue: Figure 4. Channel capacity comparison ofdifferent 2 2 MIMO systems. Finally, fig. 7 shows the performance results of a 2 4 MIMO communication system using the new decoding model compared with a 2 4 MIMO using Alamouti code, and a 2 4 MIMO usingspatial multiplexingunder the assumption that the channel is Rayleigh fading and BPSK modulation is used. The theoretical expressionof the bit error probability is as follows. P b, 2 4 MIMO Parallel decoding = 6 2 μ 5 = μ The results show the diversity order of this system approaches to N d = 6. 6 Figure 5. BER of different types of 2 3 MIMO systems. The performance Comparison of different types of 2 3MIMO systems shows that the system that uses the parallel detection algorithm achieves significantly lower BER than the system that uses spatial multiplexing technique only, but it is very close to BER of the system that uses Alamouti codeonly. On the other hand, the proposed algorithm improves the channel capacity as displayed in Fig.6. The figure shows that the channel capacity of our model is equal to the channel capacity of a similar system that uses spatial multiplexing technique, and it is more than twice the channel capacity of a similar system which usesalamouti code technique. Figure 7. BER of different types of 2 4 MIMO systems. The above analysis shows that increasing the number of receiving antenna elementssignificantly improves the reliability of 2 N r MIMO communication systems. More precisely, increasing the difference between the number of receiving and transmitting antennas improves the BER and thus the reliability of the system. Fig.8 shows these improvements. Figure 6. Channel capacity comparison ofdifferent 2 3 MIMO systems. Figure 8. BER of2 N r MIMO systems using the proposed model IJRITCC July 208, 2

5 International Journal on Recent and Innovation Trends in Computing and Communication ISSN: Volume: 6 Issue: The results in figure 8 show that the probability of bit error of a 2 4 MIMO communication system is significantly smaller than BER of the 2 2 MIMO and 2 3 MIMO systems that use the proposed parallel decoding technique. The above analysis shows the system that employs spatial multiplexing technique at the transmitter and applies the parallel detection and decoding algorithm at the receiver, improves both throughput and transmission reliability simultaneously, which cannot be achieved by other serial detection algorithms having similar dimensions. Future research may investigate the performance of the proposed algorithm using large number antenna elements at both end and when the decoding is performed on symbols in parallel manner by applying a variety types of MIMO decoding techniques. V. CONCLUSION The 5G- NR access networs are expected to provide very high capacity and ultra-reliability.to achieve these requirements, we focused on the study and investigation of MIMO communication techniques that uselarge number ofantenna elements at the receiver end of communication system and developed novels ways to enhance the overall performance of (2 N r ) MIMO systems. We proposed a new parallel detection and decoding algorithm. The performance results show that the proposed parallel detection method improves both throughput and transmission reliability simultaneously, provided that spatial multiplexing technique is used at the transmitter. In addition, it was observed that the proposed method prevents error propagation by extracting all received symbols independently and simultaneously. Also, it was shown that parallel decoding technique reduces the bit error probability and increases the speed of signal transmission and detection without using a standard STC technique. However, it was seen that the BER of the proposed algorithm is slightly higher than a similar system using STC technique. The BER performance of these two techniques become closer as N r increases and assumes value of 4 or higher in the 2 N r MIMO system. Finally, it was seen that with new decoding method the bit error probability, BER, decreases dramatically with increasing the difference between the number of receiving and transmitting antennas. We willgeneralize and investigate the performance of the proposed parallel signal processing techniquewhen using large number ofantenna elements (antenna array) at both end of MIMO communication systems. REFERENCES [] Bernard Slar. Digital Communications Fundamentals and Applications, 2 nd edition. Prentice Hall P T R. [2] B. P. Lathi and Zhi Ding. Modern Digital and Analog Communication Systems, 4th edition. Oxford University Press, Inc [3] Durgin Gregory. D. Space-Time Wireless Channels. Prentice Hall PTR, NJ07458: Pearson Education. 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