Increasing Number Of Antennas Research Articles

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment.

Multiple Symbol Differential Detection for Noncoherent Communications With Large-Scale Antenna Arrays

For communications over large-scale antennas, noncoherent differential detection is an attractive option for avoiding expensive channel estimation, but it cannot maximally collect the performance benefits as the antenna number increases. For a desired performance-complexity tradeoff, this letter develops multiple symbol differential detection (MSDD) with simple implementation for single-input multiple-output (SIMO) systems, which jointly detects a block of symbols within the channel coherence time. The generalized likelihood ratio test criterion is adopted that maximizes the likelihood function over both the information symbols and the unknown channels. Simple formulas of the MSDD-SIMO detector are derived, whose performance is competitive to training-based coherent detection.

Reduced-Complexity k-best Decoder for LTE Standard

This paper presents a VLSI implementation of reduced -complexity and reconfigurable MIMO(Multiple-Input Multiple-Output) signal detector targeting 3GPP-LTE standard. In recent wireless communication system, MIMO technology is considered as the key technique in LTE to meet the target. Maximum Likelihood (ML) detection is the optimal detection algorithm for MIMO systems. FPGA implementation of ML detector becomes infeasible as its complexity grows exponentially with the increase in number of antennas. Therefore, we propose a modified K-best detector algorithm which employs parallel and distributed sorting strategy combined with bitonic sorter that has near-ML detection solution. The design was implemented targeting Xilinx Spartan 6 device and the resource utilization results are presented and the performance comparison with the literature was also done. The total on-chip power estimated is 213mW.

Congestion control based ant colony optimization algorithm for large MIMO detection

Employing multiple antennas in wireless communication systems is a key technology for future generation of wireless systems. Symbol detection in multiple-input multiple-output (MIMO) systems with low complexity is challenging. The minimum bit error rate (BER) performance can be achieved by maximum likelihood (ML) detection. However, with increase in number of antennas in MIMO systems, the ML detection becomes impractical. For example, sphere decoder (SD) is a well known ML detector for MIMO systems, however because of its high complexity it is practical only up to 32 real dimensions. Recently, bio-inspired algorithms are being used for improving the BER performance of MIMO symbol detector, along with low complexity. In this article, we propose a congestion control based ant colony optimization (CC-ACO) algorithm for large MIMO detection. We also discuss the robustness of the proposed algorithm under channel state information (CSI) estimation error. The simulation results shows the effectiveness of the proposed algorithm in terms of achieving better bit error rate (BER) performance with low complexity.

Energy Efficiency Maximization through Cooperative Transmit and Receive Antenna Selection for Multicell MU-MIMO System

The capacity of Multiple Input Multiple Output (MIMO) system is highly related to the number of active antennas. But as the active antenna number increases, the MIMO system will consume more energy. To maximize the energy efficiency of MIMO system, we propose an antenna selection scheme which can maximize the energy efficiency of BS cluster. In the scheme, ergodic energy efficiency is derived according to large scale channel state information (CSI). Based on this ergodic energy efficiency, we introduce a cost function varied with the number of antennas, in which the effect to the energy efficiency of both the serving BS and the neighbor BS is considered. With this function, we can transform the whole system optimization problem to a sectional optimization problem and obtain a suboptimal antenna set using a heuristic algorithm. Simulation results verify that the proposed approach performs better than the comparison schemes in terms of network energy efficiency and achieves 98% network energy efficiency of the centralized antenna selection scheme. Besides, since the proposed scheme does not need the complete CSI of the neighbor BS, it can effectively reduce the signaling overhead.

Wider Aperture Sine‐Cosine Direction Finders

Higher‐order sine‐cosine azimuth patterns for Adcock direction finding are investigated to determine spacing errors, reradiation errors, and pickup factor. The higher modes achieve wider apertures and reduced wave interference error; they exhibit comparable pickup factors but require an increase in the minimum number of antennas and different sensing methods. The 1.220λ spacing limit is appropriate only to the dipole mode, the corresponding limit for the quadrupole mode being 1.684λ and correspondingly greater for the higher modes. Reduced spacing error for the minimum number of antennas occurs at increasingly wider apertures which also tend to more closely approach the theoretical limits with increasing mode. The higher modes exhibit reduced response in the vicinity of the zenith. The resolution of the additional bearing ambiguities involves the use of one or more lower‐order patterns. Predicted sense performance as a function of reradiated field amplitude is computed for a combination of quadrupole and dipole modes.