A Review to Massive MIMO Detection Algorithms: Theory and Implementation

Trotobas, Bastien; Nafkha, Amor; Louët, Yves

doi:10.5772/intechopen.93089

Cited by 15 publications

(11 citation statements)

References 33 publications

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“…The problem of optimal and efficient wireless signal detection in a Multiple-Input, Multiple-Output (MIMO) system is central to this rapid growth and has been a key interest of network designers for several decades. While the optimal Maximum Likelihood (ML) detector is well known, it attempts to solve an NP-Hard problem [6] exactly, and so its implementation is usually impractical and infeasible for real-world systems. These computational challenges have prompted network designers to seek optimized implementations such as the Sphere Decoder [7], [8], or suboptimal approximations with polynomial complexity.…”

Section: Introductionmentioning

confidence: 99%

Ising Machines’ Dynamics and Regularization for Near-Optimal MIMO Detection

Singh

Jamieson

McMahon

et al. 2022

IEEE Trans. Wireless Commun.

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Optimal MIMO detection is one of the most computationally challenging tasks in wireless systems.We show that new analog computing approaches, such as Coherent Ising Machines (CIMs), are promising candidates for performing near-optimal MIMO detection. We propose a novel regularized Ising formulation for MIMO detection that mitigates a common error floor issue in the naive approach and evolve it into a regularized, Ising-based tree search algorithm that achieves near-optimal performance. By means of numerical simulation using the Rayleigh fading channel model, we show that in principle, a MIMO detector based on a high-speed Ising machine (such as a CIM implementation optimized for latency) would allow a higher transmitter antennas (users)-to-receiver antennas ratio and thus increase the overall throughput of the cell by a factor of two or more for massive MIMO systems. Our methods create an opportunity to operate wireless systems using more aggressive modulation and coding schemes and hence achieve high spectral efficiency: for a 16 × 16 MIMO system, we estimate around 2.5× more throughput in the mid-SNR regime (≈ 12 dB) and 2× more throughput in the high-SNR regime (>20 dB) as compared to the industry standard, a Minimum-Mean Square Error (MMSE) linear decoder.

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Section: Introductionmentioning

confidence: 99%

Ising Machines’ Dynamics and Regularization for Near-Optimal MIMO Detection

Singh

Jamieson

McMahon

et al. 2022

IEEE Trans. Wireless Commun.

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“…This is also due to the complex modulation techniques employed at the transmitter. Maximum Likelihood (ML) is the optimal decoding of received symbols in a MIMO channel, as it is capable of minimizing the probability of bit errors, but it is also known to be NP-hard [8], [9]. Today's commercial massive MIMO antenna systems already contain antenna arrays large enough to face complex decoding problem.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Tabi¹,

Marosits

Kallus

et al. 2021

IEEE Access

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Quantum annealing offers an appealing route to handle large-scale optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. In particular, the novel D-Wave Advantage device has been recently released. Its performance is expected to improve upon the previous stateof-the-art D-Wave 2000Q annealer, due to higher number of qubits and the Pegasus topology. Here, we present a comparative study via an ensemble of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in Centralized Radio Access Network (C-RAN) architectures. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions mainly use approximate methodologies, Kim et al. leveraged Quantum Annealing for large MIMO problems with Phase Shift Keying and Quadrature Amplitude Modulation scenarios. Here, we extend their work and analyze experiments for more complex modulations and larger MIMO antenna array sizes. By implementing the extended QUBO formulae on the novel annealer architecture, we uncover the limits of state-of-the-art quantum optimization for the massive MIMO ML decoder. We report on the improvements and discuss the uncovered limiting factors learned from the 64-QAM extension. We include the enhanced evaluation of raw annealer sampling via implementation of post-processing methods in the comparative analysis between D-Wave 2000Q and D-Wave Advantage system.

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“… The performance of D2D-CRS-SM is analyzed and investigated, in terms of bit error rate (BER), spectral efficiency (SE), and sum SE considering M -PSK and M -QAM over independent Rayleigh fading channels. Moreover, comparative results with Vertical-Bell Laboratories layered space-time (VBLAST) spatial multiplexing MIMO technique [ 49 ] with zero-forcing (ZF) detector [ 50 ] (termed VBLAST-ZF) is also provided. The efficiency of D2D-CRS-SM is validated via Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

Device-to-Device Aided Cooperative Relaying Scheme Exploiting Spatial Modulation: An Interference Free Strategy

Kamal

Kader

Islam

et al. 2020

Sensors

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In this paper, a novel interference free dual-hop device-to-device (D2D) aided cooperative relaying strategy (CRS) based on spatial modulation (SM) (termed D2D-CRS-SM) is proposed. In D2D-CRS-SM, two cellular users (e.g., a near user (NU) and a relay-aided far user (FU)) and a pair of D2D transmitter (D1)-receivers (D2) are served in two time-slots. Two different scenarios are investigated considering information reception criteria at the NU. Irrespective of the scenarios, the base station (BS) exploits SM to map information bits into two sets: modulation bits and antenna index, in phase-1. In the first scenario, the BS maps FU information as the modulation bits and NU information as antenna index, whereas modulation bits correspond to NU information and the antenna index carries FU’s information in scenario-2. The iterative-maximum ratio combining (i-MRC) technique is then used by NU and D1 to de-map their desired information bits. During phase-2, D1 also exploits SM to forward FU’s information received from BS and its own information bits to the D2D receiver D2. Then, FU and D2 retrieve their desired information by using i-MRC. Due to exploiting SM in both phases, interference free information reception is possible at each receiving node without allocating any fixed transmit power. The performance of D2D-CRS-SM is studied in terms of bit-error rate and spectral efficiency considering M-ary phase shift keying and quadrature amplitude modulation. Finally, the efficiency of D2D-CRS-SM is demonstrated via the Monte Carlo simulation.

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A Review to Massive MIMO Detection Algorithms: Theory and Implementation

Cited by 15 publications

References 33 publications

Ising Machines’ Dynamics and Regularization for Near-Optimal MIMO Detection

Ising Machines’ Dynamics and Regularization for Near-Optimal MIMO Detection

Evaluation of Quantum Annealer Performance via the Massive MIMO Problem

Device-to-Device Aided Cooperative Relaying Scheme Exploiting Spatial Modulation: An Interference Free Strategy

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