Coherent Receiver for Turbo Coded Single-User Massive MIMO-OFDM with Retransmissions

Vasudevan, K.; Singh, Suneet; Reddy, A. Phani Kumar

doi:10.5772/intechopen.85893

Cited by 20 publications

(36 citation statements)

References 45 publications

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“…The base station and the user's initial location in the system are randomly deployed. From Figure 9, OSA and CMSA can significantly reduce the BER compared with the precoding method [37]. This is because OSA and CMSA have good anti‐interference ability.…”

Section: Simulation Resultsmentioning

confidence: 99%

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Xie¹,

Ai²

2021

IET Communications

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Sum‐rate optimization problem is a key issue in a time‐varying multi‐user multi‐input multi‐output (MU‐MIMO) distributed antenna system. Channel precoding matrix is the key technology in the achievable sum‐rate optimization problem. In this paper, two algorithms are proposed to solve the achievable sum‐rate optimization problem for a time‐varying MU‐MIMO distributed antenna system, namely one‐dimensional search algorithm (OSA) and cyclic MMSE (Minimum Mean Square Error) search algorithm (CMSA). In order to design the channel precoding matrix for the achievable sum‐rate optimization, a one‐dimensional search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. For the problem with a large amount of computation in OSA, a cyclic MMSE search algorithm is proposed in the time‐varying MU‐MIMO distributed antenna system. Simulation results show that the proposed algorithms can effectively improve the sum‐rate compared to other algorithms. Simulation results also show that OSA is superior to CMSA in the sum‐rate, and the channel state information (CSI) coefficient has little effect on OSA and CMSA.

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Section: Simulation Resultsmentioning

confidence: 99%

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Xie¹,

Ai²

2021

IET Communications

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“…SU-and MU-MIMO for distributed antenna systems (antennas that are spatially far apart) is studied in [29], which is quite different from what is presented in this work (for example see Fig. 2 of [21]).…”

Section: Introductionmentioning

confidence: 85%

“…Much of the existing literature on massive MIMO deals with multi user case (MU-MMIMO) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], where the base station is equipped with a large number of antennas and each user has only a single antenna. SU-MMIMO has not yet been studied, excepting for a few works with equal number of transmit and receive antennas and ideal receiver [19,20], orthogonal frequency division multiplexing (OFDM)-based practical receiver which estimates timing, carrier frequency offset and channel impulse response [21,22], analysis of probability of erasure (probability of not detecting an OFDM frame when it is present) [23,24], ideal receiver with unequal number of transmit and receive antennas with precoding [25,26]. SU-MMIMO is also presented in [27,28].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we describe an elegant precoding method which reduces ICI in single user massive MIMO systems and compare it with the case without precoding [19,21]. Rayleigh flat fading channel is assumed.…”

Section: Introductionmentioning

confidence: 99%

“…If the channel is frequency selective, orthogonal frequency division multiplexing (OFDM) can be used e.g. single input single output (SISO) OFDM [43,51,52], single input multiple output OFDM [53] or MIMO OFDM [21,22,54,55].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Turbo Coded Single User Massive MIMO

Vasudevan,

Reddy,

Pathak

et al. 2021

Preprint

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This work deals with turbo coded single user massive multiple input multiple output (SU-MMIMO) systems, with and without precoding. SU-MMIMO has a much higher spectral efficiency compared to multi-user massive MIMO (MU-MMIMO) since independent signals are transmitted from each of the antenna elements (spatial multiplexing). MU-MMIMO that uses beamforming has a much lower spectral efficiency, since the same signal (with a delay) is transmitted from each of the antenna elements. In this work, expressions for the upper bound on the average signal-to-noise ratio (SNR) per bit and spectral efficiency are derived for SU-MMIMO with and without precoding. We propose a performance index f (Nt), which is a function of the number of transmit antennas Nt. Here f (Nt) is the sum of the upper bound on the average SNR per bit and the spectral efficiency. We demonstrate that when the total number of antennas (Ntot) in the transmitter and receiver is fixed, there exists a minimum value of f (Nt), which has to be avoided. Computer simulations show that the bit-error-rate (BER) is nearly insensitive to a wide range of the number of transmit antennas and re-transmissions, when Ntot is large and kept constant. Thus, the spectral efficiency can be made as large as possible, for a given BER and Ntot.

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Variational-autoencoder signal detection for MIMO-OFDM-IM

Zhao

2021

Digital Signal Processing

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Coherent Receiver for Turbo Coded Single-User Massive MIMO-OFDM with Retransmissions

Cited by 20 publications

References 45 publications

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Sum‐rate optimization scheme for time‐varying distributed MU‐MIMO systems

Turbo Coded Single User Massive MIMO

Variational-autoencoder signal detection for MIMO-OFDM-IM

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