Approximate maximum a posteriori detection for multiple-input–multiple-output systems with bit-level lattice reduction-aided detectors and successive interference cancellation

2014 ◽  
Vol 8 (5) ◽  
pp. 697-706 ◽  
Author(s):  
Lin Bai ◽  
Wei Bai ◽  
Jinho Choi ◽  
Qiaoyu Li
Keyword(s):  
Interference Cancellation ◽  
Multiple Input Multiple Output ◽  
Successive Interference Cancellation ◽  
Lattice Reduction ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Multiple Input ◽  
Input Multiple Output

scholarly journals MIMO Detectors Under Correlated Channels

2016 ◽  
Vol 37 (1) ◽  
pp. 3
Author(s):  
Bruno Felipe Costa ◽  
Alex Miyamoto Mussi ◽  
Taufik Abrão
Keyword(s):  
Maximum Likelihood ◽  
Interference Cancellation ◽  
Mean Squared Error ◽  
Multiple Input Multiple Output ◽  
Successive Interference Cancellation ◽  
Correlated Channels ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Multiple Input ◽  
Input Multiple Output

Este artigo analisa o desempenho de detectores com múltiplas antenas transmissoras e múltiplas antenas receptoras (MIMO – multiple-input multiple-output) em canais com desvanecimento correlacionados. Dois esquemas de detecção MIMO denominados erro quadrático médio mínimo (MMSE – minimum mean squared error) – com ou sem a etapa de cancelamento de interferência sucessiva ordenado (OSIC – ordered successive interference cancellation) – e técnica de redução treliça (LR – lattice reduction) são analisados e comparados com o limite de detecção de máxima verossimilhança (ML – maximum likelihood) em cenários específicos de interesse: (a) com incremento da eficiência espectral através do aumento do número de antenas. (b) quando há aumento nos índices de correlação de desvanecimento do canal. Neste contexto, o desempenho do detector ótimo ML-MIMO é utilizado como referência visando caracterizar o comportamento da taxa de erro de bit (BER) destes detectores MIMO e quão próximo esses estão do desempenho ML-MIMO.

Lattice Reduction Algorithm Based Uplink MC-CDMA

2015 ◽  
Vol 738-739 ◽  
pp. 391-396
Author(s):  
Umut Yunus ◽  
Askar Hamdulla ◽  
Zhen Hong Jia ◽  
Kurban Ubul
Keyword(s):  
Communication Systems ◽  
Multiple Access ◽  
Multiple Input Multiple Output ◽  
Lattice Reduction ◽  
Rate Performance ◽  
Multiple Input ◽  
High Spectral Efficiency ◽  
Input Multiple Output ◽  
Cdma Systems ◽  
Multi Access

MC-CDMA integrates the advantages of OFDM with those of CDMA, it has high spectral efficiency, robustness against multi-path propagation and multiple access flexibility. Due to the above mentioned merits, it has been considered as a candidate for future wireless. In recent years, lattice reduction technique is discussed in multiple input multiple output communication systems, and has been shown with its better performance. The purpose of this paper is to express a model for uplink MC-CDMA systems in matrix form and then to propose a lattice reduction aided multiuser detection, in order to ameliorate the affects of inter-carrier interference and multi access interference. The effectiveness of the proposed method is evaluated by the bit error rate performance.

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

2021 ◽  
pp. 154851292110228
Author(s):  
Ravisankar Malladi ◽  
Manoj Kumar Beuria ◽  
Ravi Shankar ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Deep Learning ◽  
Interference Cancellation ◽  
Multiple Access ◽  
Multiple Input Multiple Output ◽  
Optimal Solution ◽  
Channel State ◽  
Fifth Generation ◽  
Multiple Input ◽  
Input Multiple Output ◽  
And Performance

In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

scholarly journals Efficient Power Allocation for LDPC-Coded MIMO Systems

2011 ◽  
pp. 127-133
Author(s):  
Layak Ali Sd ◽  
K. Kishan Rao ◽  
M. Sushanth Bab
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Mimo Systems ◽  
Geometric Mean ◽  
Multiple Input Multiple Output ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Coded Systems ◽  
Efficient Power ◽  
Q Function

In this papers an efficient ordering scheme for an ordered successive interference cancellation detector is determined under the bit error rate minimization criterion for multiple-input multiple-output(MIMO) communication systems using transmission power control. From the convexity of the Q-function, we evaluate the choice of suitable quantization characteristics for both the decoder messages and the received samples in Low Density Parity Check (LDPC)-coded systems using M-QAM schemes. We derive the ordering strategy that makes the channel gains converge to their geometric mean. Based on this approach, the fixed ordering algorithm is first designed, for which the geometric mean is used for a constant threshold using correlation among ordering results.

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