mimo ofdm
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2022 ◽  
Vol 70 (2) ◽  
pp. 3625-3636
Jae-Hyun Ro ◽  
Jong-Gyu Ha ◽  
Woon-Sang Lee ◽  
Young-Hwan You ◽  
Hyoung-Kyu Song

A. Gokul ◽  
J. N. Sarath ◽  
M. Mohit ◽  
M. Niranjan ◽  
Aswathy K. Nair

2021 ◽  
Yunxiang Guo ◽  
Zhenqi Fan ◽  
An Lu ◽  
Pan Wang ◽  
Dongjie Liu ◽  

Abstract In a cell-free massive MIMO system, multiple users arrive at multiple access points at separate times, while in an OFDM system, different delays can be equivalent to symbol timing offsets (STOs). Since symbol timing offsets are not all the same, in the downlink transmission process, it is necessary to consider its impact on transmission techniques, such as channel estimation and downlink precoding. In this paper, aiming at the performance loss caused by STO in cell-free massive MIMO-OFDM system, we propose a multi-RB precoding optimization algorithm that maximizes the downlink sum rate. We derive the sum rate maximization problem into an iterative second-order cone programming (SOCP) form to achieve convex approximation. Then, considering the impact of STO on the accuracy of cell-free massive MIMO-OFDM channel estimation, we propose a downlink channel estimation method, which jointly uses channel state information reference signal (CSI-RS) and demodulation reference signal (DMRS). Simulation results show that the proposed multi-RB optimal precoding can effectively improve the downlink sum rate, and the proposed downlink channel estimation can obtain accurate multi-RB frequency domain channel parameters.

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 92
Yung-Ping Tu ◽  
Chih-Yung Chen ◽  
Kuang-Hao Lin

The massive multiple-input multiple-output systems (M-MIMO) and orthogonal frequency-division multiplexing (OFDM) are considered to be some of the most promising key techniques in the emerging 5G and advanced wireless communication systems nowadays. Not only are the benefits of applying M-MIMO and OFDM for broadband communication well known, but using them for the application of the Internet of Things (IoT) requires a large amount of wireless transmission, which is a developing topic. However, its high complexity becomes a problem when there are numerous antennas. In this paper, we provide an effective two-stage multiuser detector (MUD) with the assistance of the accelerated over-relaxation (AOR) iterative algorithm and Chebyshev acceleration for the uplink of M-MIMO OFDM systems to achieve a better balance between bit error rate (BER) performance and computational complexity. The first stage of the receiver consists of an accelerated over-relaxation (AOR)-based estimator and is intended to yield a rough initial estimate of the relaxation factor ω, the acceleration parameter γ, and transmitted symbols. In the second stage, the Chebyshev acceleration method is used for detection, and a more precise signal is produced through efficient iterative estimation. Additionally, we call this proposed scheme Chebyshev-accelerated over-relaxation (CAOR) detection. Conducted simulations show that the developed receiver, with a modest computational load, can provide superior performance compared with previous works, especially in the MU M-MIMO uplink environments.

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8200
Jonathan Aguiar Soares ◽  
Kayol Soares Mayer ◽  
Fernando César Comparsi de Castro ◽  
Dalton Soares Arantes

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

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