ZF/MMSE and OSIC Detectors for UpLink OFDM Massive MIMO systems

Riadi, Abdelhamid; Boulouird, Mohamed; Hassani, Moha MrRabet

doi:10.1109/jeeit.2019.8717380

Cited by 14 publications

(6 citation statements)

References 17 publications

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“…In theory, the best receiver for data detection in MIMO systems is the Maximum Likelihood (ML) detector. However, because of the computational complexity of ML detection, the zero-forcing (ZF) or MMSE method is used for iterative detection and decoding in the actual system [40]. The CS technology we introduced can also be used to design receivers.…”

Section: B Deep Learning Decodermentioning

confidence: 99%

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Zhang

Gao

et al. 2020

IEEE Access

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In this paper, we investigate the channel estimation and decoding methods exploiting the channel sparsity in pilot-assisted Multiple-Input Multiple-Output (MIMO) Vector Orthogonal Frequency Division Multiplexing (V-OFDM) systems. Based on the sparse multipath channels, we utilize orthogonal and non-orthogonal pilot schemes to design the compressed sensing (CS) measurement process. For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with the simplified evaluation criterion to improve the pilot design procedure. Meanwhile, the effect of pilot insertion on the Peak-to-Average Power Ratio (PAPR) is reduced by a particular precoding matrix method without affecting the decoding complexity. Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method has higher channel estimation accuracy with unknown sparsity. On the other hand, to overcome the complexity of CS-based decoding, we design the fully connected Deep Neural Network (FC-DNN) decoders, which combine the results of channel estimation results with the prevalent neural network technology. We observe that when the sparse channels are estimated accurately by CS methods, the proposed FC-DNN can achieve the same performance as the high-precision linear decoder by using the time-domain pilots and channel estimation results. INDEX TERMS MIMO, OFDM, compressed sensing, channel estimation, peak to average power ratio, decoding, neural networks.

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Section: B Deep Learning Decodermentioning

confidence: 99%

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Zhang

Gao

et al. 2020

IEEE Access

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“…To select the best symbol to detect at each iteration, the OSIC detector computes the post-SNR or post-SINR for each symbol, assuming that the kth element is canceled using the detection matrix T. Most of the time, the detection matrix is chosen to be T ZF or T MMSE optionally updated after each iteration. When using the SNR criterion, the value of the kth post-SNR is computed as in [7,8].…”

Section: Successive Interference Cancellation (Sic) Detectormentioning

confidence: 99%

A Review to Massive MIMO Detection Algorithms: Theory and Implementation

Trotobas¹,

Nafkha²,

Louët³

2020

Advanced Radio Frequency Antennas for Modern Communication and Medical Systems

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Multiple-input multiple-output (MIMO) systems entered most major standards in the past decades, including IEEE 802.11n (Wi-Fi) and long-term evolution (LTE). Moreover, MIMO techniques will be used for 5G by increasing the number of antennas at the base station end. MIMO systems enable spatial multiplexing, which has the potential of increasing the capacity of the communication channel linearly with the minimum of the number of antennas installed at both sides without sacrificing any additional bandwidth or power. To handle the spacedivision multiplexing (SDM), receivers have to implement new algorithms to exploit the spatial information in order to distinguish the transmitted data streams. This chapter provides an overview of the most well-known and promising MIMO detectors, as well as some unusual-yet-interesting ones. We focus on the description of the different paradigms to highlight the different approaches that have been studied. For each paradigm, we describe the mathematical framework and give the underlying philosophy. When hardware implementations are available in the literature, we provide the results reported and give the according references.

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“…Values tested K-best K 2, 4, 8, 12, 16, 24, 32, 48, 64, 128, 192, 256 Best-first stack lengths (1,12,15), (1,7,10), (1,3,5) Sub sections. In any case, all the detectors claimed to be Pareto efficient are Pareto efficient for all the parameters from Table 4.…”

Section: Parametermentioning

confidence: 99%

“…The succesive interference cancellation (SIC) detection scheme suppress the interference by iteratively maximizing the signal-to-interference-plus-noise ratio (SNR). This approach is well suited when the received signals present different individual quality metric corresponding to each of the received data streams [6], [7]. In the opposite scenario, when data streams have similar quality parallel interference cancellation (PIC) detectors are preferred [8], [9].…”

Section: Introductionmentioning

confidence: 99%

When Should We Use Geometrical-Based MIMO Detection Instead of Tree-Based Techniques? A Pareto Analysis

et al. 2020

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The soft-output multiple-input multiple-output (MIMO) detection problem has been extensively studied, and a large number of heuristics and metaheuristics have been proposed to solve it. Unlike classical tree-search based detectors, geometrical heuristic algorithms involved two consecutive steps: (i) an exploration step based on the geometry of the channel matrix singular vectors; (ii) a local exploitation step is performed in order to obtain better final solution. In this paper, new enhancements for geometrical heuristics are introduced to significantly reduce the complexity in quadrature phase-shift keying (QPSK) and allow 16 quadrature amplitude modulation (QAM) capability through new exploration techniques. The performance-complexity trade-off between the new detector and two tree-based algorithms is investigated through Pareto efficiency. The Pareto framework also allows us to select the most efficient tuning parameters based on an exhaustive search. The proposed detector can be customized on the fly using only one or two parameters to balance the trade-off between computational complexity and bit error rate performances. Moreover, the Pareto fronts demonstrate that the new geometrical heuristic is especially efficient with QPSK since it provides a significant reduction in regards to the computational complexity while preserving good bit error rate (BER) performance and ensuring high flexibility. INDEX TERMS Geometrical detection, Pareto efficiency, Soft-output MIMO detection.

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ZF/MMSE and OSIC Detectors for UpLink OFDM Massive MIMO systems

Cited by 14 publications

References 17 publications

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

A Review to Massive MIMO Detection Algorithms: Theory and Implementation

When Should We Use Geometrical-Based MIMO Detection Instead of Tree-Based Techniques? A Pareto Analysis

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