MIMOシステムにおける格子基底縮小を用いた信号検出法及びその応用に関する研究

Multiple-input multiple-output (MIMO) technology has attracted attention in wireless communications, since it provides signi cant increases in data throughput and the high spectral efficiency. MIMO systems employ multiple antennas at both ends of the wireless link, and hence can increase the data rate by transmitting multiple data streams. To exploit the potential gains o ered by MIMO, signal processing involved in a MIMO receiver requires a large computational complexity in order to achieve the optimal performance. In MIMO systems, it is usually required to detect signals jointly as multiple signals are transmitted through multiple signal paths between the transmitter and the receiver. This joint detection becomes the MIMO detection. The maximum likelihood (ML) detection (MLD) is known as the optimal detector in terms of minimizing bit error rate (BER). However, the complexity of MLD obstructs its practical implementation. The common linear detection such as zero-forcing (ZF) or minimum mean squared error (MMSE) o ers a remarkable complexity reduction with performance loss. The non-linear detection, e.g. the successive interference cancellation (SIC), detects each symbol sequentially withthe aid of cancellation operations which remove the interferences from the received signal. The BER performance is improved by using the SIC, but is still inferior to that of the ML detector with low complexity. Numerous suboptimal detection techniques have been proposed to approximately approach the ML performance with relatively lower complexity, such as sphere detection (SD) and QRM-MLD. To look for suboptimal detection algorithm with near optimal performance and a ordable complexity costs for MIMO gains faces a major challenge. Lattice-reduction (LR) is a promising technique to improve the performance of MIMO detection. The LR makes the column vectors of the channel state information (CSI) matrix close to mutually orthogonal. The following signal estimation of the transmitted signal applies the reduced lattice basis instead of the original lattice basis. The most popular LR algorithm is the well-known LLL algorithm, introduced by Lenstra, Lenstra, and Lov asz. Using this algorithm, the LR aided (LRA) detector achieves more reliable signal estimation and hence good BER performance. Combining the LLL algorithm with the conventional linear detection of ZF or MMSE can further improve the BER performance in MIMO systems, especially the LR-MMSE detection. The non-linear detection i.e. SIC based on LR (LR-SIC) is selected from many detection methods since it features the good BER performance. And ordering SIC based on LR (LR-OSIC) can further improve the BER performance with the costs of the implementation of the ordering but requires high computational complexity. In addition, list detection can also obtain much better performance but with a little high computational cost in terms of the list of candidates. However, the expected performance of the several detections isnot satis ed directly like the ML detector, in particular for the high modulation order or the large size MIMO system. This thesis presents our studies about lattice reduction aided detection and its application in MIMO system. Our studies focus on the evaluation of BER performance and the computational complexity. On the hand, we improve the detection algorithms to achieve the near-ML BER performance. On the other hand, we reduce the complexity of the useless computation, such as the exhaustive tree search. We mainly solve three problems existed in the conventional detection methods as - The MLD based on QR decomposition and M-algorithm (QRMMLD) is one solution to relatively reduce the complexity while retaining the ML performance. The number of M in the conventional QRM-MLD is de ned as the number of the survived branches in each detection layer of the tree search, which is a tradeo between complexity and performance. Furthermore, the value of M should be large enough to ensure that the correct symbols exist in the survived branches under the ill-conditioned channel, in particular for the large size MIMO system and the high modulation order. Hence the conventional QRM-MLD still has the problem of high complexity in the better-conditioned channel. - For the LRA MIMO detection, the detection errors are mainly generated from the channel noise and the quantization errors in the signal estimation stage. The quantization step applies the simple rounding operation, which often leads to the quantization error. If this error occurs in a row of the transmit signal, it has to propagate to many symbols in the subsequent signal estimation and result in degrading the BER performance. The conventional LRA MIMO detection has the quantization problem, which obtains less reliable signal estimation and leads to the BER performance loss. - Ordering the column vectors of the LR-reduced channel matrix brings large improvement on the BER performance of the LRSIC due to decreasing the error propagation. However, the improvement of the LR-OSIC is not su cient to approach the ML performance in the large size MIMO system, such as 8 8 MIMO system. Hence, the LR-OSIC detection cannot achieve the near-ML BER performance in the large size of MIMO system. The aim of our researches focuses on the detection algorithm, which provides near-ML BER performance with very low additional complexity. Therefore, we have produced various new results on low complexity MIMO detection with the ideas of lattice reduction aided detection and its application even for large size MIMO system and high modulation order. Our works are to solve the problems in the conventional MIMO detections and to improve the detection algorithms in the signal estimation. As for the future research, these detection schemes combined with the encoding technique lead to interesting and useful applications in the practical MIMO system or massive MIMO.電気通信大学201

Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems

In this paper, we propose a MIMO receiver algorithm that exploits {\em channel hardening} that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the ${\bf H}^H{\bf H}$ matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix ${\bf H}$ increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes ${\bf H}^T{\bf H}{\bf x}$, where ${\bf x}$ is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the ${\bf H}^T{\bf H}$ matrix. We also propose a simple estimation scheme which directly obtains an estimate of ${\bf H}^T{\bf H}$ (instead of an estimate of ${\bf H}$), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the {\em channel hardening-exploiting message passing (CHEMP)} receiver. The proposed CHEMP receiver achieves very good performance in large-scale MIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of ${\bf H}$. We also present a convergence analysis of the proposed MPD algorithm. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes

Precoded Large Scale Multi-User-MIMO System Using Likelihood Ascent Search for Signal Detection

Multiple antennas at each user equipment (UE) and/or thousands of antennas at the base station (BS) comprise the extremely spectrum efficient large scale multi-user multiple input multiple output system (BS). Due to space constraints, the closely spaced numerous antennas at each UE may cause inter antenna interference (IAI). Furthermore, when one UE comes into contact with another UE in the same cellular network, multi-user interference (MUI) may be introduced to the received signal. To mitigate IAI, efficient precoding pre-coding is necessary at each UE, and the MUI present at the BS can be canceled by efficient Multi-user Detection (MUD) techniques. The majority of earlier literature deal with one or more of these interferences. This paper implements a joint pre-coding and MUD, Lenstra-Lovasz (LLL) based Lattice Reduction (LR) assisted likelihood accent search (LAS) (LLL-LR-LAS), to mitigate IAI and MUI simultaneously LLL-based LR pre-coding mitigates IAI at each UE, and the LAS algorithm is a neighborhood search-based MUD that cancels BS MUI. The proposed approaches' performance was evaluated using Bit Error Rate analysis, and their complexity were determined using multiplication and addition.Dr. Mohammad Alibakhshikenari acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant agreement No. 801538. Also, this work was supported by Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional-FEDER-, European Union) under the research Grant PID2021-127409OB-C31 CONDOR. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022)

Low-Complexity Near-Optimal Detection Algorithms for MIMO Systems

As the number of subscribers in wireless networks and their demanding data rate are exponentially increasing, multiple-input multiple-output (MIMO) systems have been scaled up in the 5G where tens to hundreds of antennas are deployed at base stations (BSs). However, by scaling up the MIMO systems, designing detectors with low computational complexity and close to the optimal error performance becomes challenging. In this dissertation, we study the problem of efficient detector designs for MIMO systems. In Chapter 2, we propose efficient detection algorithms for small and moderate MIMO systems by using lattice reduction and subspace (or conditional) detection techniques. The proposed algorithms exhibit full receive diversity and approach the bit error rate (BER) of the optimal maximum likelihood (ML) solution. For quasi-static channels, the complexity of the proposed schemes is cubic in the system dimension and is only linear in the size of the QAM modulation used. However, the computational complexity of lattice reduction algorithms imposes a large burden on the proposed detectors for large MIMO systems or fast fading channels. In Chapter 3, we propose detectors for large MIMO systems based on the combination of minimum mean square error decision feedback equalization (MMSE-DFE) and subspace detection tailored to an appropriate channel ordering. Although the achieved diversity order of the proposed detectors does not necessarily equal the full receive diversity for some MIMO systems, the coding gain allows for close to ML error performance at practical values of signal-to-noise ratio (SNR) at the cost of a small computational complexity increase over the classical MMSE- DFE detection. The receive diversity deficiency is addressed by proposing another algorithm in which a partial lattice reduction (PLR) technique is deployed to improve the diversity order. Massive multiuser MIMO (MU-MIMO) is another technology where the BS is equipped with hundreds of antennas and serves tens of single-antenna user terminals (UTs). For the uplink of massive MIMO systems, linear detectors, such as zero-forcing (ZF) and minimum mean square error (MMSE), approach the error performances of sophisticated nonlinear detectors. However, the exact solutions of ZF and MMSE involve matrix-matrix multiplication and matrix inversion operations which are expensive for massive MIMO systems. In Chapter 4, we propose efficient truncated polynomial expansion (TPE)-based detectors that achieve the error performance of the exact solutions with a computational complexity proportional to the system dimensions. The millimeter wave (mmWave) massive MIMO is another key technology for 5G cellular networks. By using hybrid beamforming techniques in which a few numbers of radio frequency (RF) chains are deployed at the BSs and the UTs, the fully-digital precoder (combiner) is approximated as a product of analog and digital precoders (combiners). In Chapter 5, we consider a signal detection scheme using the equivalent channel consisting of the precoder, mmWave channel, and combiner. The available structure in the equivalent channel enables us to achieve the BER of the optimal ML solution with a significant reduction in the computational complexity