Novel MIMO Detection With Improved Complexity for Near-ML Detection in MIMO-OFDM Systems

This paper proposes, for the first time, a two-source asymmetric turbo-coded-cooperative spatial modulation (SM) scheme over the slow Rayleigh fading channel. As in any coded cooperative communication, the interleaver plays a vital role in mitigating the harsh effect of the wireless channel. Therefore, the code matched interleaver (CMI) is effectively used in the proposed design. The simulated results reveal that the bit error rate (BER) performance of the proposed coded cooperative communication system outperforms the asymmetric turbo-coded non-cooperative scheme under identical conditions. This prominent performance improvement has been made possible due to the joint asymmetric turbo decoding at the destination node. Furthermore, to check the effectiveness of the proposed scheme, we have also developed a two-source asymmetric turbo-coded cooperative scheme based on the vertical bell labs layered space-time (VBLAST), incorporating the CMI as the suitable benchmark. It is observed that the proposed scheme employing SM has a better BER performance than the VBLAST scheme under identical conditions.

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“…T is fed into the VBLAST detector that utilizes the soft maximum likelihood detection algorithm [32]. Every element in Z denotes L × 1 vector.…”

Section: Asymmetric Turbo-coded Vblast (Atc-vblast) Scheme Based On CMImentioning

confidence: 99%

Two-Source Asymmetric Turbo-Coded Cooperative Spatial Modulation Scheme with Code Matched Interleaver

et al. 2020

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“…This number is traditionally called K for the K-best algorithm [22] or M for the M-algorithm [23]. A recent work mixed this approach with the upper-bound radius from death-first to prune even more path per level and reduce the complexity further [24]. This method converges to the breadth-first if all PED are always under the upper-bound, but if some PEDs overgrow, it can reduce the number of surviving paths.…”

Section: Breadth-first Tree-search Detectors: K-best and M-algorithmmentioning

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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“…Tree search detection algorithms are the most common nonlinear detection technology in MIMO systems. QR decomposition-based M (QRD-M) and sphere decoding (SD) are the typical detection algorithms among them [17], [18]. In [19], a multiple signalto-interference plus noise ratio (SINR) threshold is used to reduce the search space of the SD algorithm.…”

Section: Introductionmentioning

confidence: 99%

Near-ML Detection Based on Multilevel SINR Thresholding for Mode Division Multiplexing Transmission With Mode-Dependent Loss

Siyu

Zhang

et al. 2021

IEEE Access

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The probability distribution of the signal-to-interference plus noise ratio (SINR) is analyzed and approximated by the generalized gamma distribution for mode-dependent loss (MDL)-impaired fiber links. Based on the characteristics of SINR, an improved reduced-search algorithm (IRS) based on multilevel SINR thresholding (IRS-MST) is proposed. IRS-MST dynamically determines the size of the search space for each mode based on multiple SINR thresholds. Furthermore, a compounding function is used to balance the size of the search space and performance. Simulation results show that IRS-MST can achieve near ML performance for different MDLs. Compared to the conventional IRS algorithm, the complexity of IRS-MST is approximately 5.4 times smaller than that of the IRS. INDEX TERMS Mode division multiplexing, mode-dependent loss, MIMO detection, reduced search, SINR.

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Novel MIMO Detection With Improved Complexity for Near-ML Detection in MIMO-OFDM Systems

Cited by 17 publications

References 22 publications

Two-Source Asymmetric Turbo-Coded Cooperative Spatial Modulation Scheme with Code Matched Interleaver

Two-Source Asymmetric Turbo-Coded Cooperative Spatial Modulation Scheme with Code Matched Interleaver

A Review to Massive MIMO Detection Algorithms: Theory and Implementation

Near-ML Detection Based on Multilevel SINR Thresholding for Mode Division Multiplexing Transmission With Mode-Dependent Loss

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