Capacity-Achieving Iterative LMMSE Detection for MIMO-NOMA Systems

This paper considers a iterative Linear Minimum Mean Square Error (LMMSE) detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO) systems with Non-Orthogonal Multiple Access (NOMA). The iterative LMMSE detection greatly reduces the system computational complexity by departing the overall processing into many low-complexity distributed calculations. However, it is generally considered to be sub-optimal and achieves relatively poor performance. In this paper, we firstly present the matching conditions and area theorems for the iterative detection of the MIMO-NOMA systems. Based on the proposed matching conditions and area theorems, the achievable rate region of the iterative LMMSE detection is analysed. We prove that by properly design the iterative LMMSE detection, it can achieve (i) the optimal sum capacity of MU-MIMO systems, (ii) all the maximal extreme points in the capacity region of MU-MIMO system, and (iii) the whole capacity region of two-user MIMO systems.Comment: 6pages, 5 figures, accepted by IEEE ICC 2016, 23-27 May 2016, Kuala Lumpur, Malaysi

Gaussian Message Passing for Overloaded Massive MIMO-NOMA

This paper considers a low-complexity Gaussian Message Passing (GMP) scheme for a coded massive Multiple-Input Multiple-Output (MIMO) systems with Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station with $N_s$ antennas serves $N_u$ sources simultaneously in the same frequency. Both $N_u$ and $N_s$ are large numbers, and we consider the overloaded cases with $N_u>N_s$. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. First, we prove that the \emph{variances} of the GMP definitely converge to the mean square error (MSE) of Linear Minimum Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of the traditional GMP will fail to converge when $N_u/N_s< (\sqrt{2}-1)^{-2}\approx5.83$. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any $N_u/N_s>1$, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results presented.Comment: Accepted by IEEE TWC, 16 pages, 11 figure

A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution towards 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, etc. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication

Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information (CSI) is investigated to provide the required quality of service (QoS) for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original non-convex power minimization problem to a tractable one. Further, a fairness-based beamforming approach is proposed through a max-min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original non-convex problem is reformulated in a linear matrix inequality (LMI) form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the non-robust scheme in terms of the rate satisfaction ratio. Further, simulation results confirm that NOMA consumes a little over half transmit power needed by OMA for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog

非直交多元接続のための高信頼空間変調

　マルチユーザ空間変調（SM: Spatial Modulation）では，SM信号の疎性を用いた圧縮センシングによるマルチユーザ検出が研究されているものの，受信機においてチャネル情報が完全に既知であるという条件の下で議論されている．実際には受信機側でチャネル情報を推定し，推定したチャネル情報を用いて復調処理を行う．推定したチャネル情報の精度は復調の精度に影響を及ぼすため，チャネル推定は重要なものであり考慮しなければならない．　そこで本研究ではチャネル推定を，ブロックスパース性を有する信号の再構成問題として扱い，ブロックスパース性を考慮した複素数近似メッセージ伝播法（BS-CAMP: Block-Sparse Complex Approximate Message Passing）によって信号の再構成を行う方法を提案する．BS-CAMPは受信機が送信信号に含まれる非零要素の個数を事前に知る必要がない再構成アルゴリズムとなっており，ランダムアクセス方式にも適用可能である．計算機シミュレーションより，BS-CAMPによるチャネル推定の精度やスループット特性への影響を示す．　さらに，高信頼な通信を実現するにはチャネル推定だけでなく誤り訂正符号が重要となる．そこで併せて本研究ではSMに誤り訂正符号化を組み合わせたものの一つである，ターボトレリス符号化空間変調（SM-TTC: SM with Turbo Trellis-Coding）における符号の最適化及び性能解析を行った．具体的には，シンボルベースEXIT（Extrinsic Information Transfer）チャートを用いた低演算符号探索法によって，演算量を低減しながら最良の特性を示す符号を探索する．計算機シミュレーションより，探索した符号を用いたSM-TTCが従来のものよりも優れていること，および提案手法が従来の符号探索法よりも低計算量で符号探索が可能であることを示す．電気通信大学201