Iterative group detection and decoding for large MIMO systems
TL;DR: A soft detection algorithm for MIMO systems which performs close to the full dimensional joint detection, yet offers significant complexity reduction over the existing detectors.
Abstract: Recently, a variety of reduced complexity soft-in soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-in soft-output detectors for MIMO systems due to heavy burden in computational complexity. In this paper, we propose a soft detection algorithm for MIMO systems which performs close to the full dimensional joint detection, yet offers significant complexity reduction over the existing detectors. The proposed algorithm, referred to as soft-input soft-output successive group (SSG) detector, detects a subset of symbols (called a symbol group) successively using a deliberately designed preprocessing to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of the interfering symbol groups successively using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results on realistic MIMO systems demonstrate that the proposed SSG detector achieves considerable complexity reduction over the conventional approaches with negligible performance loss.
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TL;DR: Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree.
Abstract: Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin.
10 citations
TL;DR: In this paper, the authors consider designing demodulators for linear vector channels that use reduced-size trellis descriptions for the received signal and propose two types of CS demodulator that are based on the Forney and Ungerboeck detection models, respectively.
Abstract: We consider designing demodulators for linear vector channels that use reduced-size trellis descriptions for the received signal We assume an iterative receiver and use interference cancellation (IC) with soft-information provided by an outer decoder to mitigate the signal part that is not covered by a reduced-size trellis description In order to reach a trellis description, a linear filter is applied as a front end to compress the signal structure into a small trellis This process requires three parameters to be designed: 1) the front-end filter; 2) the feedback filter through which the IC is done; and 3) a target response which specifies the trellis Demodulators of this form have been studied before under the name channel shortening (CS), but the interplay between CS, IC, and the trellis-search processes has not been adequately addressed in the literature In this paper, we analyze two types of CS demodulators that are based on the Forney and Ungerboeck detection models, respectively The parameters are jointly optimized with a generalized mutual information (GMI) function We also introduce a third type of CS demodulator that is, in general, suboptimal, which has closed-form solutions Furthermore, signal-to-noise ratio asymptotic properties are analyzed, and we show that the third CS demodulator asymptotically converges to the optimal CS demodulator in the sense of GMI maximization
3 citations
TL;DR: The numerical results demonstrate that the GAIL algorithm can achieve close-to-optimal performance while maintaining low computational complexity and the running speed can be dramatically increased using parallel processing in real-time communication systems.
Abstract: In this paper, we propose a low-complexity group alternate iterative list (GAIL) detection algorithm for MIMO systems. By utilizing the recursive interference suppression and successive interference cancellation techniques, the symbol vector can be partitioned into many subgroups. Subsequently, symbols in each subgroup are detected in terms of the K-best detector. The inter-group interference is effectively mitigated in the GAIL algorithm by creating a candidate list and iteratively correcting the unreliable symbols for the detection result. We provide the performance-complexity tradeoff based on different feasible parameter settings. The numerical results demonstrate that the GAIL algorithm can achieve close-to-optimal performance while maintaining low computational complexity. In addition, the running speed of the GAIL algorithm can be dramatically increased using parallel processing in real-time communication systems.
2 citations
Cites methods from "Iterative group detection and decod..."
...These group detection methods split the symbol vector into multiple subgroups and then detect subgroups separately [16]–[19]....
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References
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TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Abstract: (1995). Fundamentals of Statistical Signal Processing: Estimation Theory. Technometrics: Vol. 37, No. 4, pp. 465-466.
14,342 citations
Book•
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01 Aug 1998
TL;DR: This self-contained and comprehensive book sets out the basic details of multiuser detection, starting with simple examples and progressing to state-of-the-art applications.
Abstract: From the Publisher:
The development of multiuser detection techniques is one of the most important recent advances in communications technology. This self-contained and comprehensive book sets out the basic details of multiuser detection, starting with simple examples and progressing to state-of-the-art applications. The only prerequisites assumed are undergraduate-level probability, linear algebra, and digital communications. The book contains over 240 exercises and will be a suitable textbook for electrical engineering students. It will also be an ideal self-study guide for practicing engineers, as well as a valuable reference volume for researchers in communications, information theory, and signal processing.
5,048 citations
29 Sep 1998
TL;DR: This paper describes a wireless communication architecture known as vertical BLAST (Bell Laboratories Layered Space-Time) or V-BLAST, which has been implemented in real-time in the laboratory and demonstrated spectral efficiencies of 20-40 bps/Hz in an indoor propagation environment at realistic SNRs and error rates.
Abstract: Information theory research has shown that the rich-scattering wireless channel is capable of enormous theoretical capacities if the multipath is properly exploited In this paper, we describe a wireless communication architecture known as vertical BLAST (Bell Laboratories Layered Space-Time) or V-BLAST, which has been implemented in real-time in the laboratory Using our laboratory prototype, we have demonstrated spectral efficiencies of 20-40 bps/Hz in an indoor propagation environment at realistic SNRs and error rates To the best of our knowledge, wireless spectral efficiencies of this magnitude are unprecedented and are furthermore unattainable using traditional techniques
3,925 citations
"Iterative group detection and decod..." refers methods in this paper
...Symbol Ordering and Grouping In the first step of the SSG detection, the symbols are ordered and processed based on post-detection SINR [23], which is beneficial for the remaining groups to be detected and also reduces the chance of error propagation....
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...Note that the post-detection SINR-based symbol ordering [23] is performed before the LSD, K-best detection, and SSG detection....
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...STEP 1: (Symbol ordering and grouping) Order symbols x according to post-detection SINR [23]....
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TL;DR: Using log-likelihood algebra, it is shown that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and aPriori inputs, and the extrinsic value.
Abstract: Iterative decoding of two-dimensional systematic convolutional codes has been termed "turbo" (de)coding. Using log-likelihood algebra, we show that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes but also for any linear binary systematic block code. The iteration is controlled by a stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes for lower rates less than 2/3. Any combination of block and convolutional component codes is possible. Several interleaving techniques are described. At a bit error rate (BER) of 10/sup -4/ the performance is slightly above or around the bounds given by the cutoff rate for reasonably simple block/convolutional component codes, interleaver sizes less than 1000 and for three to six iterations.
2,632 citations