Sử dụng bộ mã phân cấp tại máy thu nhằm nâng cao dung lượng kênh trong truyền thông sóng MM

Nội dung text: Sử dụng bộ mã phân cấp tại máy thu nhằm nâng cao dung lượng kênh trong truyền thông sóng MM

1. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) USING HIERARCHICAL CODEBOOK AT THE RECEIVER TO IMPROVE THE CHANNEL CAPACITY IN MM WAVE COMMUNICATION SỬ DỤNG BỘ MÃ PHÂN CẤP TẠI MÁY THU NHẰM NÂNG CAO DUNG LƯỢNG KÊNH TRONG TRUYỀN THÔNG SÓNG MM Tran Hoai Trung University of Transport and Commnications, Hanoi, Vietnam Ngày nhận bài: 30/03/2021, Ngày chấp nhận đăng: 12/06/2021, Phản biện: TS. Dương Thị Thanh Tú Abstract: Mm-wave is currently of interest due to its ability to enhance large bandwidth, which can fully meet the speed of 5G information systems. The beamforming techniques are studied for this wave such as beam weights updating at the receiver to ensure the fastest convergence rate of the weighting algorithm. We need to specify algorithms such as mean least squares (LMS), recursive least squares (RLS), sampling matrix inverse (SMI), conjugate gradient methods (CGM). There are many articles related to the comparison of these algorithms on convergence weighting speed, transmission capacity. However, finding a matrix of receive beams in a multi-path environment is a problem of little interest. Using the hierarchical codebook (HC) is applied to produce suitable gain-weight vectors and requires fewer iterative steps to find beam weights. In addition, compared with the traditional methods mentioned above, such as LMS, RLS, SMI, or CGM, the method of finding beam weights using HC also ensures the increased channel capacity, especially with the high signal-to-noise ratios. Mm wave communication, beamforming, LMS, RLS, SMI, CGM algorithms, HC, channel capacity. Keywords: Truyền thông sóng mm, beamforming, thuật toán LMS, RLS, SMI, CGM, HC, dung lượng kênh truyền. Tóm tắt: Sóng mm hiện đang được quan tâm do khả năng tăng cường được băng thông lớn, hoàn toàn có thể đáp ứng được tốc độ cho hệ thống thông tin 5G. Các kỹ thuật beamforming được nghiên cứu cho sóng này như cập nhật trọng số bức xạ tại máy thu với mục đích đảm bảo tốc độ hội tụ thuật toán tìm trọng số là nhanh nhất. Chúng ta cần chỉ rõ các thuật toán như bình phương trung bình nhỏ nhất (LMS), bình phương nhỏ nhất đệ quy (RLS), nghịch đảo ma trận lấy mẫu (SMI), phương pháp đạo hàm liên hợp (CGM). Có nhiều bài liên quan tới việc so sánh các thuật toán này về tốc độ tìm trọng số hội tụ, dung lượng truyền. Tuy nhiên việc tìm một ma trận bức xạ thu trong môi trường đa đường là bài toán ít được quan tâm. Giải pháp sử dụng bộ mã phân cấp (HC) được áp dụng để đưa ra các véc tơ trọng số thu phù hợp và đòi hỏi số bước lặp tìm trọng số bức xạ không cao. Ngoài ra so với các phương pháp truyền thống kể trên như LMS, RLS, SMI hay CGM thì phương pháp tìm trọng số bức xạ sử dụng HC còn đảm bảo được dung lượng kênh truyền tăng cao, đặc biệt với những tỷ số tín hiệu trên nhiễu SNR cao. Số 26 101
2. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) 1. INTRODUCTION The adaptive beamforming is requested The current mm-wave is of great interest for the 5G communication system. when its frequency range is from 30 GHz Adaptive beamformer performs spatial to 300 GHz, ultimately meeting the signal processing compatibly, which channel capacity of the 5G system. consists of an array of transmitting and Although it is affected by propagation receiving antennas [1-2]. conditions a lot, it is an advantage when Popular adaptive beamforming methods we can deploy multiple antenna elements at the transmitter and receiver because the include LMS, RLS, SMI, and CGM. antenna size is inversely proportional to Among them, the LMS beamforming the frequency. This opportunity opens the method is relatively simple and has door to exciting antenna techniques such been implemented in many radio as beamforming or spatial multiplexing. communication applications [2]. Fading channel Receiver Component Figure 1. Transceiver architecture This method can perform beamforming One method used to compensate for the that does not require matrix inversion as Doppler effect is to combine the antenna used in the SMI method. It uses a fixed array at the receiver [3]. It improves beam step size for beamforming. It makes weights changes over time can be updated using RLS algorithm. Although RLS is LMS stable and simple. Therefore this more complex than LMS, it can converge method is often used for many different more quickly. It even converges faster applications. However, LMS has the than SMI [1-2]. The RLS offers an MSE slowest convergence rate among the that is smaller than the LMS and methods mentioned above [1]. Normalised LMS (NLMS) methods, 102 Số 26
3. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) guaranteed within the allowable 2. CURRENT METHODS FOR FINDING threshold, while SMI has the lowest MSE BEAM WEIGHTS [1], [4-7]. In terms of complexity, RLS With the channel model used for mm has better temporal complexity but less waves, we see that the received signal is spatial complexity than SMI [8-10]. generalized as follows: However, the complexity depends on the algorithms' coding, and SMI is optimized x WH HFd n (1) for spatial complexity. For a small number of antenna elements, the SMI Where d is the transmit signal vector, W algorithm has better quality in space, time is the received beam matrix, F is the and MSE than RLS. However, the NLMS, transmitted beam matrix, H is the LMS methods are all steepest descent channel matrix. gradient-based iterative algorithms, while The paper presents two figures to simplify SMI and RLS are recursive and block the description of algorithms. Figure 1 compatible methods, respectively. These methods do not have as high a depicts the structure of a channel model convergence capacity as CGM because it with the one transmit antenna and the N is difficult to determine the vector’s value receive antennas. Figure 2 describes a by eigenvalue when it is spread. receiver scheme using a received beam CGM allows convergence even faster vector. than the above methods where it uses perpendicular search while the other methods use eigenvalues with a large spread. One method used for multipath Output signal environments is to use the hierarchical codebook [11]. It shows better energy efficiency and beam weights search - capabilities than traditional methods like + Design Adaptive signal the exhausitive search. In this article, the Algorithm author introduces the beam weights search algorithm using hierarchical Figure 2. Current beam weight finding methods codebook and proves the ability to LMS Algorithm [2-3]: converge fast, increase capacity compared w 0 0 to the LMS, RLS, CGM and SMI methods through simulation. For n=0, 1, 2, Số 26 103
4. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) w n 1 w n  e* n x n (2) For n=1, 2, H where A x n x n * T T e n d n w n x n is error n r n r n / p n Ap n vector w n 1 w n n p n (5) 2  is step size r n 1 r n n Ap n 3tr R xx T T SMI Algorithm [2]:  n r n 1 r n 1 / r n r n 1 k H p n 1 r n 1  n p n R xx n  x n x n k n 1 Proposed CGM Algorithm in [2]: 1 k *  rxs n s n x n r 1 d 1 ones(N) Aw k n 1 with A x11 xH k is number of observations I have: p 1 r 1 1 For n=2,3, w n Rxx n r xs n (3) RLS Algorithm [3]: n H R n  x l x l l 1 w 0 0 w n w n 11 n r n (6) For n=1, 2, P n 1 x* n Với r n p n n r n 1 k n  x T n P n 1 x* n p n p n 1 n R n r n 1 T e n d n w n 1 x n r H n 1 f n 1 n r H n 1 R n f n 1 w n w n 1 e n k n (4) 1 f n R n r n 1 P n P n 1 k n xT n P n 1  n r H n 1 R n r n 1  k Traditional CGM Algorithm [2]: 3. PROPOSED HIERARCHICAL CODEBOOK ALGORITHM r 1 d 1 ones(N) Aw 1 S 2 with A x 11 xH 0 H fd 0 p 1 r 1 for l = 1 : L do 104 Số 26
5. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) (searching two transmit and receive 4. SIMULATION codewords) Based on the above algorithms, we S for m = 1 : 2 0 do simulate a 4 4 MIMO antenna model for n = 1 : 2S0 do used for 5G mobile communication systems. Here, the required number of y m,n loops for each algorithm and the H H Pw r S0 ,n H z Pw r S0 ,n Hfd generation of beamforming through the end beam weight vectors. In the simulation, we use three physical paths with input end parameters such: mt ,nr arg max y m,n m,n Gain of paths : 1 0.5 0.5; for s = S0 1 : S do AoDs: 0.5 /8 1.5 /8 14.5 /8; for m = 1,2 do AoAs:  0.5 /8 1.5 /8 14.5 /8; for n = 1,2 do Wavelength of signal:  0.01(m); Velocity of mobile:  40 (km/h); H y m,n Pw r s,2 n r 1 n H z Transmit antenna spacing: s 0.05 (m); T H Pw r s,2 n r 1 n Hfd Receive antenna spacing: sR 0.05 (m); end Number of elements at transmit antenna: M=4 (using one beam formed by 4 end antenna elements); Number of elements at a,b arg max y m,n receive antenna: N=4. Number of training m,n symbols is 20 while number of data symbols is 30. mt 2 mt 1 a;n r 2 n r 1 b end for m = 1,0,1 do for n = 1,0,1 do y Pw s, n n H H z r r H H yw s, n n fd fd r r Figure 3. LMS: MSE and beam weights end With Figure 3, after ten attempts of end creating the random input, the number of end iterations is unstable and often significant. Số 26 105
6. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) Figure 4. RLS : MSE and beam weights Figure 7. SMI: beam weights As for Figure 4, the number of iterations is lower than the LMS but still high. It can take up to 6 iterations to have the optimal beam weights. Figure 8. HC: beam weights Figures 7 and 8 show two methods SMI and HC, respectively, The SMI describes the process of the inverse correlation Figure 5. Tra. CGM : MSE and beam weights matrix to find weight beams. This correlation matrix changes with the In Figure 5, the number of iterations of number of observations, so if we have traditional CGM is two, proving that the 20 observations, we will have 20 convergence of this method is better than corresponding beams for Figure 7. In the LMS, RLS. case of the SMI, we take the last beam (20th beam) for the capacity simulation in Figure 9. In the case of the HC case, there are three paths, so after using an improved search algorithm, we can find three optimum beams, which are also used in the simulation of Figure 9. In addition to being interested in the Figure 6. Pro. CGM : MSE and beam weights number of iterations to find the optimum The Proposed CGM shown in Figure 6 beam weights, we are also interested in has the number of iterations of 1, which is how the beams generated by these lower than Tranditional CGM so the weights affect the channel capacity. This convergence speed is faster. is because the weight vectors will affect 106 Số 26
7. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) the antenna gain in the receive directions gives the highest channel capacity in SNR in a multipath environment. We compare with large values of about 4-5 dB the capacity for LMS, RLS [3], traditional onwards. and proposed CGM [2], and the proposed HC in this paper instances. 5. CONCLUSION The article covers the fundamental algorithms such as LMS, RLS, Traditional and Proposed CGM, and Proposed HC. We compare the capacity for LMS, RLS, traditional and proposed CGM, and the proposed HC. The proposed HC allows capacity to be significantly increased, especially with Figure 9. Capacities in cases of LMS, RLS, Tra. And Pro. CGM [2], SMI và Proposed HC the high SNR. The article also shows how to generate receive beams using a In Figure 9, we see that the channel hierarchical codebook for multipath capacity in the case of LMS and RLS is environments. similar, although it is proven that RLS has a faster convergence rate than LMS, ACKNOWLEDGMENT Traditional CGM has a higher channel This article is implemented as a requirement capacity than Proposed CGM [2], of the University-level research project, although the convergence speed Proposed T2021-DT-009. I thank the University of CGM's is faster. SMI has better capacity Transport and Communications for this than the two cases above, but the new HC support. REFERENCE [1] SK Imtiaj, Iti Saha Misra, Sandipan Bhattacharya, Revisiting smart antenna array design with multiple interferers using basic adaptive beamforming algorithms: Comparative performance study with testbed results, Wiley, 2020. [2] Veerendra Dakulagi, Mukil Alagirisamy, Adaptive Beamformers for High-Speed Mobile Communication, Wireless Personal Communications volume, Vol.113, No.7, 2020, pp. 1691–1707. [3] Irma Zakia; Suhartono Tjondronegoro; Iskandar; Adit Kurniawan, Performance comparison of LMS and RLS adaptive array on high speed train delivered from High Altitude Platforms, International Conference of Information and Communication Technology (ICoICT), 2013. [3] [4] Sonia Hokam, Anjali Dandekar, Ankita Tiwari, Gulafshaafreen Sheikh, An Overview of LMS Adaptive Beamforming Algorithm for Smart Antenna, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, Issue. 1, 2016. Số 26 107
8. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) [5] Joseph Paulin Nafack Azebaze, Elijah Mwangi, Dominic B.O. Konditi, Performance Analysis of the LMS Adaptive Algorithm for Adaptive Beamforming, International Journal of Applied Engineering Research, Vol. 12, No. 22, 2017, pp. 12735-12745. [6] Revati Joshi, Adaptive Beamforming Using LMS Algorithm, International Journal of Research in Engineering and Technology, Vol. 03, No. 05, 2014, pp.589-593. [7] Vivek Kumar, Deepak Rajouria, Manju Jain, Vikas Kumar, Performance Analysis of LMS Adaptive Beamforming Algorithm, IJECT, Vol. 4, Issue Spl - 5, 2013. [8] Muhammad, C. B.; Anwar, K., Interference Mitigation using Adaptive Beamforming with RLS Algorithm for Coexistence between 5G and Fixed Satellite Services in C-Band, Journal of Physics: Conference Series, Vol. 1577, Issue. 1, 2020. [9] Peter Chuku, Thomas Olwal and Karim Djouani, Adaptive Array Beamforming Using An Enhanced RLS Algorithm, International Journal on AdHoc Networking Systems (IJANS), Vol. 8, No. 1, 2018. [10] L. Wang R.C. de Lamare, Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming, IET Signal Processing, Vol. 4, No. 6, 2011, pp. 686 – 697. [11] Zhenyu Xiao, Tong He, Pengfei Xia and Xiang-Gen Xia, Hierarchical Codebook Design for Beamforming Training in Millimeter-Wave Communication, IEEE Transactions on Wireless Communications, Vol.15, Issue.5, 2016, pp. 3380-3392. Biography: Tran Hoai Trung received the B.E degree University of Transport and Communications (UTC) in 1997 and the Master degree from Hanoi University of Science and Technology (HUST) in 2000. He received the Ph.D degree of Telecommunication engineering at University of Technology, Sydney (UTS), Australia in 2008. He is currently lecturer at the UTC, Vietnam. His research interests are digital signal processing (DSP), applied information theory, radio propagation, MIMO antenna techniques and advanced wireless transceiver design. 108 Số 26
9. TẠP CHÍ KHOA HỌC VÀ CÔNG NGHỆ NĂNG LƯỢNG - TRƯỜNG ĐẠI HỌC ĐIỆN LỰC (ISSN: 1859 - 4557) Số 26 109