List of Articles Massive MIMO

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  1 - Complexity Reduction in Massive-MIMO-NOMA SIC Receiver in Presence of Imperfect CSI
  Nilufar Tutunchi Afrooz Haghbin Behrad Mahboobi
  10.29252/jist.8.30.113
  One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-or More

  One of the main reasons for switching to the next generation of communication systems is the demand of increasing capacity and network connections. This goal can be achieved using massive multiple input - multiple output (massive-MIMO) systems in combination with Non-orthogonal multiple access (NOMA) technique. NOMA technology uses the successive interference cancellation (SIC) receiver to detect user’s signals which imposes an additional complexity on the system. In this paper, we proposed two methods to reduce the system complexity. The proposed method despite imperfect channel state information (CSI) in the receiver, there is not significantly reduction in the system performance. Since the computation of matrices inverse has a high computational complexity, we used the Neumann series approximation method and the Gauss-Seidel decomposition method to compute matrices inverse in the SIC receiver. Simulation results are provided at the end of the paper in terms of bit error rate (BER) at the receiver which show, these methods have lower computational complexity in comparison with the traditional methods while they cause a slight performance reduction in the SIC receiver. Also, we examined the increasing and decreasing value of imperfect channel state information in the system performance which shows the increasing value of imperfect channel state information, cause a slight performance reduction in SIC receiver. Manuscript profile
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  2 - Low-Complexity Iterative Detection for Uplink Multiuser Large-Scale MIMO
  Mojtaba Amiri Mahmoud Ferdosizade Naeiny
  10.7508/jist.2020.01.003
  In massive Multiple Input Multiple Output (MIMO) or large scale MIMO systems, uplink detection at the Base Station (BS) is a challenging problem due to significant increase of the dimensions in comparison to ordinary MIMO systems. In this letter, a novel iterative metho More

  In massive Multiple Input Multiple Output (MIMO) or large scale MIMO systems, uplink detection at the Base Station (BS) is a challenging problem due to significant increase of the dimensions in comparison to ordinary MIMO systems. In this letter, a novel iterative method is proposed for detection of the transmitted symbols in uplink multiuser massive MIMO systems. Linear detection algorithms such as minimum-mean-square-error (MMSE) and zero-forcing (ZF), are able to achieve the performance of the near optimal detector, when the number of base station (BS) antennas is enough high. But the complexity of linear detectors in Massive MIMO systems is high due to the necessity of the calculation of the inverse of a large dimension matrix. In this paper, we address the problem of reducing the complexity of the MMSE detector for massive MIMO systems. The proposed method is based on Gram Schmidt algorithm, which improves the convergence speed and also provides better error rate than the alternative methods. It will be shown that the complexity order is reduced from O(〖n_t〗^3) to O(〖n_t〗^2), where n_t is the number of users. The proposed method avoids the direct computation of matrix inversion. Simulation results show that the proposed method improves the convergence speed and also it achieves the performance of MMSE detector with considerable lower computational complexity. Manuscript profile
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  3 - A New Power Control Algorithm in MMSE Receiver for D2D Underlying Massive MIMO System
  Faezeh  Heydari Saeed Ghazi-Maghrebi Ali Shahzadi Mohammad Jalal  Rastegar Fatemi
  10.52547/jist.9.34.141
  20.1001.1.23221437.2021.9.34.4.1
  Device to device (D2D) underlying massive MIMO cellular network is a robust deployment which enables network to enhance its throughput. It also improves services and applications for the proximity-based wireless communication. However, an important challenge in such dep More

  Device to device (D2D) underlying massive MIMO cellular network is a robust deployment which enables network to enhance its throughput. It also improves services and applications for the proximity-based wireless communication. However, an important challenge in such deployment is mutual interference. Interference, in the uplink spectrum, reusing the same resource with cellular user, is caused by D2D users. In this paper, we study a distributed power control (DPC) algorithm, using minimum mean square error (MMSE) filter in receiver, to mitigate the produced interference in this deployment scenario. For the DPC algorithm, employing the coverage probability of D2D links, an optimal power control approach is proposed, which maximizes the spectral efficiency of D2D links. Using this modeling approach, it is possible to derive closed-form analytical expressions for the coverage probabilities and ergodic spectral efficiency, which give insight into how the various network parameters interact and affect the link.‎ Also, the DPC algorithm is modeled by stochastic geometry and receiver filter is designed by estimation theory that a new structure in this robust network is an approach to improve spectral efficiency. Simulation results illustrate enhancing coverage probability performance of D2D links in term of the target (signal to interference ratio) SIR with respect to different receiver filter and other parameters which are existing in D2D links. Manuscript profile
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  4 - A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems
  Mojtaba Amiri Amir Akhavan
  10.52547/jist.34192.10.40.249
  20.1001.1.23221437.2022.10.40.8.4
  Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spe More

  Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods. Manuscript profile