Suited architecture for massive MIMO detector based on antenna selection and LAS algorithm

Massive MIMO is an extension of MIMO (Multiple-input Multiple-output), which essentially groups together several number of transmitting antennas and receiving antennas in order to provide better spectrum efficiency and better throughput. Massive MIMO technology will almost be a key component of the super-fast 5G networks of the future generation. However, the Multiple User Interference (MUL) is the major problem that needs to be address to detect each user appropriately at the base station. Hence, detection of encoded signals is one of the crucial receiver functions in MIMO wireless communication. The well-known detection algorithms like Zero forcing detector (ZF) and MMSE provides less complex design at the cost of reduced performance. Maximum Likelihood detector (ML) gives the optimal performance, but its computational complexity limits its usage in practical systems. Thus, to balance the trade-off between complexity and performance, the suboptimal Likelihood Ascent Search (LAS) algorithm has been proposed. However, the LAS algorithm detection performance depends on selection of initial solution. So, we propose Neighborhood search algorithm to select the initial vector and this healthy initial vector can be used for LAS algorithm to find solution. This algorithm is iterative in nature and it searches for that vector which minimizes the maximum likelihood (ML) cost in the neighborhood. The proposed algorithm is expected to balance the tradeoff between performance and complexity of design.

We consider a wireless transmission scheme based on randomized space-time spreading (STS) systems over massive multiple-input multiple-output (MIMO) channels. In the systems, the signals are spread by spreading matrices over time and antenna domains at the transmitter. We propose that the spreading matrices are generated from binary pseudo-noise (PN) and mutual quasi-orthogonal sequences, by partitioning each of the sequences into subsequences treated as real and imagination parts of rows or columns of a spreading matrix. We give a likelihood ascent search (LAS) detector for the STS systems and analyze its computational complexity. The number of multiplication and division operations for the LAS detector does not depend on its iterative process and the number of chips in the bit period. We use Monte Carlo method to solve the bit error rates (BERs) of the STS systems and to count numbers of evaluating bits in the LAS detectors. We choose the system parameters, such as the number of chips in the bit period and the transmission bit rates, according to BER performance and/or the numbers of evaluating bits attained from Monte-Carlo method. With the spreading matrices, the detection, and the suitable parameters, the STS systems achieve BERs near to their single-bit performance, which is the BER of a STS system with a single bit spread by orthonormal sequences at each transmitting antenna. We also compare our systems with V-BLAST-like spatial multiplexing systems with BPSK signals to show the improvement of the BER performance.

In this paper, we propose a new space-time modulation scheme using quadrature amplitude modulation division for a two-user massive multiple-input multiple-output (MIMO) uplink system, in which channel state information (CSI) is unavailable at both the transmitter and the receiver. It is proved that our proposed transmission scheme enables both the large scale channel coefficients and the transmitted symbols to be uniquely and explicitly determined in a noise-free case when the number of the receiver antennas tends to infinity. For a noisy channel with a limited number of the receiver antennas, three different detectors including a simple closed-form detector, a minimum Euclidean distance detector, and a minimum Riemannian distance detector, are investigated. In addition, when the distribution of the large scale channel coefficients is known to the transmitter, a power loading algorithm is proposed. Finally, computer simulations demonstrate that our proposed communication scheme performs very well in terms of bit error rate (BER) with a moderate number of base station (BS) antennas.

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

Interference is considered as the major problem in the next-generation wireless systems to attain higher throughput. The conventional interference cancelation techniques attain a theoretical breach in endorsing the wireless systems. However, the interference in the signals causes an error or imprecise estimation of the channel for future fifth-generation (5 G) transmissions. Thus, this paper proposes a technique, named Fractional Sequential Likelihood Ascent Search (Fractional-SLAS) for mitigating the interference in massive Multiple Input Multiple Output (MIMO) systems in 5 G technology. The proposed Fractional-SLAS is the integration of Fractional calculus (FC) and Sequential Likelihood Ascent Search Detector (SLAS). Initially, the signals are transmitted through the transmitter and undergo the encoding and modulation process. Then, the obtained modulated signals are fetched using the transmitting antenna that transmits the signals to the receiver. At the receiver side, interference is generated due to the number of cross antennas. Thus, the interference in the signals is eradicated by applying the proposed Fractional-SLAS at the receiver end. Hence, the proposed interference mitigation method is completely eradicated from the signals without any delay or loss in the quality of the transmitted signal. The proposed Fractional-SLAS provides superior performance with minimal Bit Error Rate (BER), Symbol Error Rate (SER), and throughput with values 0.00006 dB, 0.00006 dB, and 0.96 bps.

The development of massive multiple-input multiple-output (MaMIMO) techniques is motivated by the requirements of large spectral efficiency and reduced power consumption. The implementation of a large number of antennas offers a large spectral efficiency and link reliability. Moreover, the use of MaMIMO allows scaling down the transmitted power proportionally to the number of antennas used, which may lead to a significant improvement in terms of energy efficiency. Orthogonal frequency division multiplexing (OFDM) in combination with MaMIMO has a considerable potential to obtain very high data rates and high quality of service (QoS). However, MaMIMO systems require a mobile equipped with multiple antennas at the transmitter. This is a challenging issue in mobile devices mostly due to their size, cost, and computing power limitations. In MaMIMO, the radiated power per antenna decreases linearly with the number of antennas. Moreover, the effects of small-scale fading, non-coherent interference, and receiver noise are minimized. However, a massive number of antennas require a separate transceiver chain and power amplifier (PA) for each antenna (unless analog or hybrid analog-digital structures are used for beamforming purposes, in which case the number of RF chains can be reduced). In this situation, the size and costs of the analog front-end become a critical issue. The cost and size optimization implies the use of low-cost components which increase the imperfections that degrade the system performance. In this chapter, MaMIMO system performance, considering front-end RF imperfections and ADC/DAC with limited resolution, is carefully studied. Spectral and energy efficiency for the uplink and downlink scenarios are evaluated to quantify the overall system performance. Finally, low-resolution precoding techniques, antenna coupling, channel non-reciprocity, and channel estimation errors are also addressed.

Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.

Outage capacity analysis of the massive MIMO diversity channel

The promise of future 5G networks is to provide very high capacity and ultra-reliable communication. Performance analysis of wireless systems is a key towards insightful design of current and future wireless systems. In this paper, we investigate the uplink performance of a single cell multi-user massive multiple-input multiple-output (MIMO) system over inverse-Gamma shadowing channels. Assuming perfect knowledge of the channel state information (CSI), we derive closed-form expressions for outage probability, average achievable rate and bit error rate (BER). Additionally, we obtain an approximate expression of the signal-to-interference-plus-noise ratio with imperfect CSI, and we derive the corresponding outage probability expression. The obtained expressions are assessed through numerous simulations and numerical examples that confirm the accuracy of our analysis. Furthermore, the results give a clear view on the impact of various parameter settings (including fading severity, transmit power, and the number of base station antennas) on the system’s performance.

With a large number of antennas at the transmitter and receiver, massive multiple-input multiple-output (MIMO) systems can achieve substantially high spectral efficiency as well as high stability and reliability. Nevertheless, one of the major challenges for its practical use is its computational complexity for symbol detection that grows exponentially with the number of antennas. In this work, we focus on a low complexity implementation of massive MIMO detector based on matched filter (MF) detection with parallel interference cancellation (PIC). In general, MF-based detector performs poorly in the high SNR region. Nevertheless, with the help of powerful channel coding such as low-density parity-check (LDPC) codes and the well-designed PIC, the resulting error performance of the proposed MF-PIC can approach or even outperform the conventional minimum mean square error (MMSE)-based detector with much less complexity. Furthermore, due to its parallel structure, low latency implementation based on parallel architecture is possible and thus is suitable for practical massive MIMO systems.

Multiple Input Multiple Output (MIMO) low complexity receiver which utilizes the compressive sensing detection for Spatial Modulation in large scale MIMO system in order to reduce the system complexity. In conventional MIMO system; huge amount of antennas is used at both ends to exploit the multipath propagation. This system maximizes the throughput performance and data rates are increased but only at the cost of high hardware complexity and increased power-consumption. Spatial Modulation Matching Pursuit (SMMP) is the proposed enhanced CS technique used for the improvement of detection performance. Hence, this paper reviews recent research findings concerning normalized Compressive Sensing (CS) detection algorithm, used for Spatial Modulation (SM) in massive MIMO, to lowers the signal processing complexity, which in result improves the energy efficiency of system against that of conventional MIMO system.This strategy is achieved by involving additional structures and sparsity in which a single transmitter antenna or a subset of it is turned on at each case to transmit a certain data. The subset of antenna which is turned on for transmission depends on approaching data bits. Therefore, the total increase in the spectral efficiency of the system is given as base-two logarithm of whole antennas at the transmitter. It reduces the signal processing load at base station and doesn’t depend upon any synchronization between transmitters. The Spatial Modulation Matching Pursuit used prevents the Inter Channel Interference (ICI) of the system which in result improves the Bit Error Rate (BER) performance than the typical MIMO system.

Massive Multiple Input Multiple Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). To reduce the system complexity and hardware cost, low complexity antenna selection techniques can be used to choose the best antenna subset while keeping the system performance at a certain required level. In this paper, Tabu Search (TS) and three bio-inspired optimization algorithms were used for antenna selection in Massive MIMO systems. The three bio-inspired algorithms were: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Simulations showed promising results for the TS by achieving higher capacity with GA than PSO and ABC, and much shorter CPU time than any of the bio-inspired techniques.

Low-complexity linear precoders are known to be close to optimal for massive multiple-input multiple-output (M-MIMO) systems. However, the large number of antennas at the transmitter imposes a high computational burden and high hardware overloads. In line with this, in this paper, we propose a low-complexity antenna selection (AS) scheme that selects the antennas that maximize constructive interference between the users. Our analyses show that the proposed AS algorithm, in combination with a simple matched-filter (MF) precoder at the transmitter, is able to achieve better performances than systems equipped with a more complex channel inversion (CI) precoder and computationally expensive AS techniques. First, we give an analytical definition of constructive and destructive interference, based on the phase of the received signals from phase-shift-keying-modulated transmissions. Then, we introduce the proposed AS algorithm, which identifies the antenna subset with the highest constructive interference, maximizing the power received by the user. In our study, we derive the computational burden of the proposed technique with a rigorous and thorough analysis, and we identify a closed-form expression of the upper bound received power at the user side. In addition, we evaluate in detail the power benefits of the proposed transmission scheme by defining an efficiency metric based on the achieved throughput. The results presented in this paper prove that AS and green radio concepts can be jointly used for power-efficient M-MIMO, as they lead to significant power savings and complexity reductions.

Massive multiple-input multiple-output (MIMO) systems can achieve high data rates due to the large number of antennas at the base station when it serves a small number of users. However, deploying massive MIMO in current mobile networks faces a lot of practical issues such as limited physical space and power consumption. Packing large number of antennas in a limited physical space makes spatial correlation between the base station antennas inevitable. In this paper, we study the effect of correlation and we investigate the effectiveness of increasing the number of antennas given limited physical space. Hybrid analog and digital MIMO decoding is adopted to exploit the large array gain at a lower cost and power consumption. Also, different antenna-array structures are investigated and compared in terms of power consumption versus performance.

Massive multiple-input multiple-output (MIMO) systems have attracted much attention lately due to the many advantages they provide over single- antenna systems. Owing to the many antennas, low- cost implementation and low power consumption per antenna are desired. To that end, massive MIMO structures with one-bit analog-to-digital converters (ADC) have been investigated in many studies. However, the effect of a strong interferer in the adjacent band on one-bit quantized massive MIMO systems have not been examined yet. In this study, we analyze the performance of uplink massive MIMO with one-bit ADCs under frequency selective fading with orthogonal frequency division multiplexing (OFDM) in the perfect and imperfect receiver channel state information cases. We derive analytical expressions for the bit error rate and ergodic rate. We show that the interfering band can be suppressed by increasing the number of antennas or the oversampling rate when a zero-forcing receiver is employed.