This paper presents an iterative soft decision based lattice reduction (LR) aided Schnorr-Euchner (SE) multiple-input-multiple-output (MIMO) decoding algorithm, which reduces the gap in performance between suboptimal ...
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ISBN:
(纸本)9781479969593
This paper presents an iterative soft decision based lattice reduction (LR) aided Schnorr-Euchner (SE) multiple-input-multiple-output (MIMO) decoding algorithm, which reduces the gap in performance between suboptimal k-best and maximum likelihood (ML) detectors. Following IEEE 802.16e standard, we develop an iterative soft decoding algorithm for 4 x 4 MIMO with different modulation schemes. Using this method, we obtain 1.1 to 2.7 dB improvement over iterative soft decision based least sphere decoding (LSD) for different iterations. Then, using extensive simulation, we determine the optimum values for list size and saturation limit, which are the two governing parameters of our algorithm. Finally, we demonstrate that limiting the log likelihood ratio (LLR) values in LR-aided and LSD algorithm results in more than 8x reduction in list size as well as in the complexity of detectors and LLR calculation units.
Machine-type communications are quite often of very low data rate and of sporadic nature and therefore not well-suited for nowadays high data rate cellular communication systems. Since signaling overhead must be reaso...
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ISBN:
(纸本)9780992862619
Machine-type communications are quite often of very low data rate and of sporadic nature and therefore not well-suited for nowadays high data rate cellular communication systems. Since signaling overhead must be reasonable in relation to message size, research towards joint activity and data estimation was initiated. When the detection of sporadic multi-user signals is modeled as a sparse vector recovery problem, signaling concerning node activity can be avoided as it was demonstrated in previous works. In this paper we show how well-known k-best detection can be modified to approximately solve this finite alphabet Compressed Sensing problem. We also demonstrate that this approach is robust against parameter variations and even works in cases where fewer measurements than unknown sources are available.
Linear detectors such as zero-forcing (ZF) and minimum mean square error (MMSE) require only a small fraction of computational complexity compared to maximum likelihood (ML) detector. However, linear detections suffer...
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ISBN:
(纸本)9781424492688
Linear detectors such as zero-forcing (ZF) and minimum mean square error (MMSE) require only a small fraction of computational complexity compared to maximum likelihood (ML) detector. However, linear detections suffer from severe performance degradation. In this paper, we propose a novel detection scheme which obtains the initial symbol detection by MMSE detector and then perform symbol ordering by signal-to-interference-and-noise ratio (SINR). The MMSE detected symbols with higher SINR are retained as part of final solution and cancelled from the original received signals. The remaining symbols with lower SINR are detected by k-best algorithm, which selects kbest nodes in each layer of the partial tree search. The small value of k is sufficient to achieve good performances, and therefore the extra computational complexity is minimal. Simulation results show the performance superiority of the proposed method compared to the conventional MMSE detection. Moreover, at the similar symbol error rates, the total number of nodes visited in the proposed approach is much smaller than the conventional k-best detection scheme.
Because of their lower complexity and better error performance over k-best detectors, lattice-reduction (LR)-aided k-best detectors have recently proposed for large-scale multi-input multi-output (MIMO) detection. Amo...
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ISBN:
(纸本)9780992862619
Because of their lower complexity and better error performance over k-best detectors, lattice-reduction (LR)-aided k-best detectors have recently proposed for large-scale multi-input multi-output (MIMO) detection. Among existing LR-aided k-best detectors, complex LR-aided k-best detector is more attractive compared to its real counterpart due to its potential lower latency and resources. However, one main difficulty in hardware implementation of complex LR-aided k-best is to efficiently find top k children of each layer in complex domain. In this paper, we propose and implement an LR-aided k-best algorithm that efficiently finds top k children in each layer when k is relatively small. Our implementation results on Xilinx VC707 FPGA board show that, with the aid of LR, the proposed LR-aided k-best implementation can support 3 Gbps transmissions for 16x16 MIMO systems with 1024-QAM with about 2.7 dB loss to the maximum likelihood detector at bit-error rate 10(-4).
In this paper, a practical pipelined k-best lattice decoder featuring efficient operation over infinite complex lattices is proposed. This feature is a key element that enables it to operate at a significantly lower c...
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ISBN:
(纸本)9781424414833
In this paper, a practical pipelined k-best lattice decoder featuring efficient operation over infinite complex lattices is proposed. This feature is a key element that enables it to operate at a significantly lower complexity than currently reported schemes. The main innovation is a simple means of expanding/visiting the intermediate nodes of the search tree on-demand, rather than exhaustively or approximately, and also directly within the complex-domain framework. In addition, a new distributed sorting scheme is developed to keep track of the best candidates at each search phase;the combined expansion and sorting cores are able to find the kbest candidates in just k clock cycles. Its support of unbounded infinite lattice decoding distinguishes our work from previous k-best strategies and also allows its complexity to scale sub-linearly with modulation order. Since the expansion and sorting cores cooperate on a data-driven basis, the architecture is well-suited for a pipelined parallel VLSI implementation of the proposed k-best lattice decoder. Comparative results demonstrating the promising performance, complexity and latency profiles of our proposal are provided in the context of the 4x4 MIMO detection problem.
Global energy consumed by communication and information technologies is expected to increase rapidly due to continuous usage of wireless standards and the expansion for their requirements [1]. In the next generation w...
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Global energy consumed by communication and information technologies is expected to increase rapidly due to continuous usage of wireless standards and the expansion for their requirements [1]. In the next generation wireless communications, Multi Input and Multi Output (MIMO) systems are most promising technology to achieve high spectral efficiencies, while going past various challenges like resource and energy constraints [2]. There exists many detection algorithms like Maximum Likelihood (ML), Zero Forcing (ZF), Minimum Mean Square Error (MMSE) which have low silicon complexity but consume significant power for high-end MIMO systems, due to their high computational complexity. And then there are certain low power detection algorithms like real domain breadth first search k-best, with either conventional enumeration or Schnorr Euchner (SE) based enumeration. This improvement through either, comes with cost of comparatively high silicon complexity and sacrifices the performance in terms of detection bit error rate (BER). The complex domain equivalent may improve the BER performance but it’s dedicated algorithm ensures even higher silicon complexity. Several modifications have been performed on original complex domain k-best algorithm to decrease its high silicon complexity, retaining the better performance of the system. This work focuses on study and implementation of original real SE based k-best algorithm [3]. It also features my attempt to perform theoretical analysis of original complex domain detection algorithm, and to implement modified [4] and improved versions of complex domain to decrease its high silicon complexity, retaining BER performance. This work also focuses on exploration and implementation of past attempts on design modifications of complex domain algorithms and compare them across different attributes such as performance, computational and silicon complexity. Few system level and algorithmic level enhancements have been proposed and implemented
Machine-type communications are quite often of very low data rate and of sporadic nature and therefore not well-suited for nowadays high data rate cellular communication systems. Since signaling overhead must be reaso...
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ISBN:
(纸本)9781479946037
Machine-type communications are quite often of very low data rate and of sporadic nature and therefore not well-suited for nowadays high data rate cellular communication systems. Since signaling overhead must be reasonable in relation to message size, research towards joint activity and data estimation was initiated. When the detection of sporadic multiuser signals is modeled as a sparse vector recovery problem, signaling concerning node activity can be avoided as it was demonstrated in previous works. In this paper we show how well-known k-best detection can be modified to approximately solve this finite alphabet Compressed Sensing problem. We also demonstrate that this approach is robust against parameter variations and even works in cases where fewer measurements than unknown sources are available.
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