398 research outputs found

    Multiuser Detection by MAP Estimation with Sum-of-Absolute-Values Relaxation

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    In this article, we consider multiuser detection that copes with multiple access interference caused in star-topology machine-to-machine (M2M) communications. We assume that the transmitted signals are discrete-valued (e.g. binary signals taking values of ±1\pm 1), which is taken into account as prior information in detection. We formulate the detection problem as the maximum a posteriori (MAP) estimation, which is relaxed to a convex optimization called the sum-of-absolute-values (SOAV) optimization. The SOAV optimization can be efficiently solved by a proximal splitting algorithm, for which we give the proximity operator in a closed form. Numerical simulations are shown to illustrate the effectiveness of the proposed approach compared with the linear minimum mean-square-error (LMMSE) and the least absolute shrinkage and selection operator (LASSO) methods.Comment: submitted; 6 pages, 7 figure

    Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems

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    Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms

    Sparse Signal Processing Concepts for Efficient 5G System Design

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    As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

    Soft-decision equalization techniques for frequency selective MIMO channels

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    Multi-input multi-output (MIMO) technology is an emerging solution for high data rate wireless communications. We develop soft-decision based equalization techniques for frequency selective MIMO channels in the quest for low-complexity equalizers with BER performance competitive to that of ML sequence detection. We first propose soft decision equalization (SDE), and demonstrate that decision feedback equalization (DFE) based on soft-decisions, expressed via the posterior probabilities associated with feedback symbols, is able to outperform hard-decision DFE, with a low computational cost that is polynomial in the number of symbols to be recovered, and linear in the signal constellation size. Building upon the probabilistic data association (PDA) multiuser detector, we present two new MIMO equalization solutions to handle the distinctive channel memory. With their low complexity, simple implementations, and impressive near-optimum performance offered by iterative soft-decision processing, the proposed SDE methods are attractive candidates to deliver efficient reception solutions to practical high-capacity MIMO systems. Motivated by the need for low-complexity receiver processing, we further present an alternative low-complexity soft-decision equalization approach for frequency selective MIMO communication systems. With the help of iterative processing, two detection and estimation schemes based on second-order statistics are harmoniously put together to yield a two-part receiver structure: local multiuser detection (MUD) using soft-decision Probabilistic Data Association (PDA) detection, and dynamic noise-interference tracking using Kalman filtering. The proposed Kalman-PDA detector performs local MUD within a sub-block of the received data instead of over the entire data set, to reduce the computational load. At the same time, all the inter-ference affecting the local sub-block, including both multiple access and inter-symbol interference, is properly modeled as the state vector of a linear system, and dynamically tracked by Kalman filtering. Two types of Kalman filters are designed, both of which are able to track an finite impulse response (FIR) MIMO channel of any memory length. The overall algorithms enjoy low complexity that is only polynomial in the number of information-bearing bits to be detected, regardless of the data block size. Furthermore, we introduce two optional performance-enhancing techniques: cross- layer automatic repeat request (ARQ) for uncoded systems and code-aided method for coded systems. We take Kalman-PDA as an example, and show via simulations that both techniques can render error performance that is better than Kalman-PDA alone and competitive to sphere decoding. At last, we consider the case that channel state information (CSI) is not perfectly known to the receiver, and present an iterative channel estimation algorithm. Simulations show that the performance of SDE with channel estimation approaches that of SDE with perfect CSI

    Support Recovery with Sparsely Sampled Free Random Matrices

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    Consider a Bernoulli-Gaussian complex nn-vector whose components are Vi=XiBiV_i = X_i B_i, with X_i \sim \Cc\Nc(0,\Pc_x) and binary BiB_i mutually independent and iid across ii. This random qq-sparse vector is multiplied by a square random matrix \Um, and a randomly chosen subset, of average size npn p, p[0,1]p \in [0,1], of the resulting vector components is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models where \Um is typically %A16 the identity or a matrix with iid components, to allow \Um satisfying a certain freeness condition. This class of matrices encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verd\'u, as well as a number of information theoretic bounds, to study the input-output mutual information and the support recovery error rate in the limit of nn \to \infty. We also extend the scope of the large deviation approach of Rangan, Fletcher and Goyal and characterize the performance of a class of estimators encompassing thresholded linear MMSE and 1\ell_1 relaxation

    Gurafikaru moderujo no kakuritsu suiron to tsushinro fukugo mondai e no oyo ni kansuru kenkyu

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    制度:新 ; 報告番号:甲3279号 ; 学位の種類:博士(理学) ; 授与年月日:2011/2/25 ; 早大学位記番号:新558

    非直交多元接続のための高信頼空間変調

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     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
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