Massive Multiple-input Multiple-output Uplink Research Articles

Low-Complexity BFGS-Based Soft-Output MMSE Detector for Massive MIMO Uplink

For the massive multiple-input multiple-output (MIMO) uplink, the linear minimum mean square error (MMSE) detector is near-optimal but involves undesirable matrix inversion. In this paper, we propose a low-complexity soft-output detector based on the simplified Broyden–Fletcher–Goldfarb–Shanno method to realize the matrix-inversion-free MMSE detection iteratively. To accelerate convergence with minimal computational overhead, an appropriate initial solution is presented leveraging the channel-hardening property of massive MIMO. Moreover, we employ a low-complexity approximated approach to calculating the log-likelihood ratios with negligible performance losses. Simulation results finally verify that the proposed detector can achieve the near-MMSE performance with a few iterations and outperforms the recently reported linear detectors in terms of lower complexity and faster convergence for the realistic massive MIMO systems.

Receive antenna selection for MMSE signal detection in multiuser massive MIMO uplink

The problem of arbitrarily selecting only some of the antennas for signal reception in the multiuser massive multiple-input multiple-output (MIMO) uplink is examined. A binary quadratic program (BQP) based on maximizing the sum of signal-to-interference and noise ratio (SINR) is formulated to fulfill this task. It can then be relaxed to be convex and solved efficiently. Simulation results show that using the proposed antenna selection together with the minimum mean-squared error (MMSE) signal detection, if the number of receive antennas is reduced from N to N a , its error-rate performance can be better than that of the system originally equipped with N a receive antennas employing the same MMSE signal detection.

A Joint Equalization and Decoding Technique for Multiuser Massive MIMO Uplink System With Transmitter and Receiver RF Impairments Under Doubly Selective Channels

The impairments due to in-phase and quadrature-phase imbalance at the transmitter and/or receiver, carrier frequency offset, and doubly selective channel (DSC) result in multiuser interference (MUI) and self-intercarrier interference (S-ICI) in multiuser massive multiple-input–multiple-output (MIMO) uplink systems. The MUI and S-ICI significantly deteriorate the performance of the receiver and make data detection quite a challenging task. To tackle this issue, we developed a novel crisscross banded effective channel matrix and a joint equalization and detection technique for multiuser massive MIMO single-carrier frequency division multiple access/orthogonal frequency division multiple access uplink systems with RF impairments under DSC. During each iteration of the proposed method, the MUI and S-ICI are calculated using the soft information from the decoder section, and then the interference canceled signal is fed back to the equalizer section for the joint equalization of the mirror subcarrier pairs. Since the proposed method does not put any restriction on the allocation of mirror subcarrier pairs, it is equally applicable to all types of carrier assignment schemes. Further, the extrinsic information transfer chart of the proposed technique is developed for analyzing the convergence. Extensive simulation studies are conducted to validate the performance of the proposed technique.

Evolutionary gravitational Neocognitron neural network based differential spatial modulation detection scheme for uplink multiple user huge MIMO systems

Interfering manipulation is a growing number of bright aspects for 6th generation (6G) infrastructure, particularly to detect huge sequence quadrature amplitude modulation (QAM) signals. In this manuscript, an evolutionary gravitational Neocognitron neural network based differential spatial modulation detection scheme (EGNNN-DSMDS) is proposed for uplink multiple user huge multiple input multiple output (MIMO) systems. To prevent the wastage of antenna resources, massive MIMO uplink systems use the differential spatial modulation (DSM), here the transmitter is equipped with a small count of antennas, whereas the receiver is equipped with a huge count of antennas. By utilizing the asymptotic characteristics of massive multiple input multiple output channels, the transmitting signal can be uniquely decoded without instantaneous channel state information in the receiver. When analyzed with conventional coherent zero-forcing detector (ZFD), the proposed approach has less complication. The proposed EGNNN-DSMDS detection scheme achieves excellent bit error ratio performance and estimating channel errors. EGNNN-DSM attains higher spectral efficiency 99.56%, 96.33%, 98.11%, lower BER 2.5%, 4.13%, 1.19% and lower computational complexity 1.19%, 2.16%, 1.15% compared with existing methods, like MLPNN-MUD-UMM-MIMO, CNN-LASD-UMM-MIMO, and DNN-SRD-UMM-MIMO. Finally, the proposed EGNNN-DSM detection scheme attains the better outcomes.

A Dual-Function Massive MIMO Uplink OFDM Communication and Radar Architecture

This paper proposes a joint uplink massive multiple-input-multiple-output (MIMO) communication and orthogonal frequency-division multiplexing (OFDM) radar sensing architecture. Specifically, uplink communication and short-range radar sensing are considered, where the user equipments (UEs) transmit data to the base-station (BS), which simultaneously receives radar returns from the targets over the same subcarriers. Hence, the signals received at each BS antenna include radar returns and communication signals to be processed for extracting the sensing and communication data. The separation and detection of such signals are achieved by utilizing the channel diversity between the UEs and the targets. To this end, the UEs’ signals are first detected and demodulated, and then subtracted from the received signal to acquire the radar returns. Symbol-based radar processing is then employed, as it provides substantial processing gains, and its detection performance is independent of the transmitted radar waveform. Furthermore, self-interference—due to the simultaneous operation of transmit and receive antennas—is taken into account. The communication rate and normalized error of the radar-target channel estimation are mathematically analyzed, and the trade-off between the achievable rate and radar detection performance is demonstrated in terms of the output power of the communication and radar sub-systems and the signal-to-noise ratio.

Energy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithm

Given that, the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell M-MIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is determined according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.

Eavesdropping in the Uplink of massive MIMO: countermeasure and performance analysis

In massive multiple-input multiple-output (MIMO) systems, a multi-antenna eavesdropper could potentially harm the uplink security by channel estimation in the training phase and then coherent detection in the payload data phase. To address this issue, we propose to inject artificial noise to the payload data to combat eavesdropping. Specifically, lower bounds of secrecy rates for maximum-ratio combining (MRC) and zero-forcing (ZF) detection schemes are derived. In each case, we further investigate scenarios with perfect and imperfect channel-state information. Moreover, the impact of key parameters, including the channel estimation error, power allocation factor and the number of antennas on the security of massive MIMO uplink are discussed.

Hardware Topologies for Decentralized Large-Scale MIMO Detection Using Newton Method

Centralized Massive Multiple Input Multiple Output (MIMO) uplink detection techniques for baseband processing possess severe bottleneck in terms of interconnect bandwidth and computational complexity. This problem has been addressed in the current work by adapting the centralized Newton method for decentralized MIMO uplink detection leveraging several Base Station antenna clusters. The proposed decentralized Newton (DN) method achieves error-rate performance close to centralized Zero Forcing detector as compared to other decentralized techniques. Two hardware topologies, namely the ring and the star topologies, are proposed to assess and discuss the trade-off among interconnect bandwidth and throughput, in comparison with contemporary decentralized MIMO uplink detection techniques. As such the following findings are elaborated. On BS antenna cluster scaling for different MIMO system configurations, the ring topology provides high throughput at constant interconnect bandwidth, while the star topology provides lower latency with a deterministic variation in the hardware resource consumption. Due to strategic optimizations on the hardware implementation, additional user equipment can be allotted at a fractional increase in Field Programmable Gate Array resource consumption, improved energy efficiency, and increased transaction of bits per Joule. The ring topology can process additional subcarrier at a fractional increase in latency and improved system throughput.

A 0.58-mm2 2.76-Gb/s 79.8-pJ/b 256-QAM Message-Passing Detector for a 128 × 32 Massive MIMO Uplink System

Massive multiple-input-multiple-output (MIMO) detection uses a large number of antennas to increase spectral efficiency at a cost of large computation resources and power in a base station. In this article, we demonstrate a 0.58-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 128×32 (it denotes 128 base station antennas and 32 single-antenna users) 256-QAM massive MIMO uplink detector based on message-passing detection (MPD). With the proposed symbol hardening processing, the complexity is reduced by more than 60% compared to the direct implementation of MPD. The detector implements a grouped layer-parallel architecture to accelerate convergence, enabling an average throughput of 2.76 Gb/s (running, on average, 4.92 iterations with early termination) at 221 mW. The chip incorporates adaptive precision control and clock gating to improve energy efficiency further by up to 43%.

Spectral efficiency analysis for massive MIMO orthogonal frequency division multiplexing and filter bank multicarrier in a realistic scenario using signal‐to‐noise‐and‐distortion ratio and interpolation for different modulation coding schemes

AbstractThe study investigates the self‐equalization effect and its impact on the spectral efficiency (SE) analysis in a massive multiple‐input multiple‐output (MIMO) scenario for 5G wireless networks for filter bank multicarrier (FBMC) and orthogonal frequency division multiplexing (OFDM) modulations. For that the SNR adopted in previous studies is compared to the inverse of the mean squared error (MSE) for multiple users in a single cell environment. In order to incorporate other distortions not correlated to the signal, this work uses the inverse of the MSE as the signal‐to‐noise‐and‐distortion ratio (SNDR) and, that metric is adopted for SE calculation in massive MIMO (MM) uplink setup. Throughout the article, the importance of considering this metric for the SE analysis of the MM system will be noticeable. Previous works dealing with MM focus on the case when the number of antennas is so much higher than the number of users that specific conditions could be applied, however, it was shown throughout this study that some of those assumptions could represent an imprecision, especially for the case when the ratio between the number of antennas and the number of users is less expressive.

A Low Complexity Detector for Massive MIMO Uplink Systems

Fifth generation wireless communication system is implemented by several mobile companies where important technologies are employed such as the massive multiple-input multiple-output (MIMO). Although it improves the spectrum and energy efficiencies, it is not a trivial to propose an efficient detector for massive MIMO systems because a large number of antennas increases the computational complexity. In this paper, approximate message passing (AMP) is exploited in the massive MIMO uplink (UL) data detection. Although the AMP method is suffering from a considerable performance in special cases, an efficient selection of the initial solution affects greatly the convergence rate, the bit-error-rate performance, and the computational complexity. In this paper, three efficient initialization methods of the AMP-based massive MIMO UL detector are proposed based on iterative methods, namely, the successive overrelaxation, the Gauss–Seidel, and the Jacobi. In addition, a stair matrix is exploited to achieve high convergence rate. Numerical results show that the proposed detector achieved a considerable performance improvement and complexity reduction in comparison with the traditional AMP-based detector.

Cooperative PSK constellation design and power allocation for massive MIMO uplink communications

This paper considers a massive multiple-input multiple-output (MIMO) wireless uplink communication system over Rayleigh fading channels. In this system, two single-antenna nodes timely upload data to a base station (BS) with a large number of antennas on the same time-frequency resources. The small scale fading coefficients keep constant during two consecutive time slots, after which they change into new independent values. Assuming only the large-scale fading coefficients are available, we construct a maximum likelihood (ML) detector without the instantaneous small scale fading coefficients at the BS. The key idea is to utilize the absolutely additively uniquely decomposable constellation pair with a phase-shift keying (PSK) modulation scheme. Specifically, we use a rotated PSK constellation for one of the nodes to ensure the unique identification of transmitted symbols in a noise-free case. In a noisy case, we propose a power allocation framework for the proposed modulation scheme to improve the ML detector's average symbol error performance. Finally, simulations results verify the effectiveness of our proposed scheme.

Comparative Analysis of Data Detection Techniques for 5G Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is a backbone technology in the fifth-generation (5G) and beyond 5G (B5G) networks. It enhances performance gain, energy efficiency, and spectral efficiency. Unfortunately, a massive number of antennas need sophisticated processing to detect the transmitted signal. Although a detector based on the maximum likelihood (ML) is optimal, it incurs a high computational complexity, and hence, it is not hardware-friendly. In addition, the conventional linear detectors, such as the minimum mean square error (MMSE), include a matrix inversion, which causes a high computational complexity. As an alternative solution, approximate message passing (AMP) algorithm is proposed for data detection in massive MIMO uplink (UL) detectors. Although the AMP algorithm is converging extremely fast, the convergence is not guaranteed. A good initialization influences the convergence rate and affects the performance substantially together and the complexity. In this paper, we exploit several free-matrix-inversion methods, namely, the successive over-relaxation (SOR), the Gauss–Seidel (GS), and the Jacobi (JA), to initialize the AMP-based massive MIMO UL detector. In other words, hybrid detectors are proposed based on AMP, JA, SOR, and GS with an efficient initialization. Numerical results show that proposed detectors achieve a significant performance enhancement and a large reduction in the computational complexity.

Low-Complexity Near-Optimal Iterative Signal Detection Based on MSD-CG Method for Uplink Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) wireless system is increasingly becoming a vital factor in fifth-generation (5G) communication systems. It is attracting considerable interest due to improve range, spectral efficiency, and coverage as compared to the conventional MIMO systems. In massive MIMO systems, the maximum likelihood detector achieve the optimum performance but it has exponential complexity for realistic antenna configurations systems, Moreover, Linear detectors commonly suffer from a matrix inversion which is not hardware-friendly. There is an increase in the computational complexity associated with the unique benefits of the massive MIMO systems. The system might be classified as an ill-conditioned problem and hence, the signal cannot be detected. To reduce the data detection complexity, we investigate a linear detector based on the multiple search direction conjugate gradient (MSD-CG) in the massive MIMO uplink systems. Several theoretical iterative techniques that can be used to balance complexity and performance for massive MIMO detection have been proposed in the literature. These methods whose convergence rate for common applications is slow where there is a decrease in the base station to user antenna ratio. In this paper, the performance of the CG method has been advanced by a projection method that necessitates a search direction in each sub-domain instead of making all search directions conjugate to each other. In this regard, our results show that the proposed algorithm with realistic antenna configurations is superior to the existing methods in terms of computational complexity for large-scale MIMO systems.

Improved quantum algorithm for MMSE-based massive MIMO uplink detection

In this paper, we propose an improved quantum algorithm for the minimum mean square error-based massive multiple-input multiple-output (MIMO) uplink. The new algorithm can reduce the dependency on the assumptions on the input vector, the channel matrix entries and the low rank of the channel matrix, which are indispensable in our previous results. Our improved quantum algorithm applies the quantum block-encoding technology, which depends on the quantum-accessible data structure. Moreover, we design an efficient algorithm for outputting classical data, which makes sure that output data can be utilized in classical devices. Both theoretically mathematical analyses and simulation realizations in massive MIMO systems confirm the applicability of the improved quantum algorithm. With desired precision, and theoretical and numerical analysis, our improved quantum algorithm can achieve a quadratic or even an exponential speedup over classical counterparts.

Spectral- and Energy-Efficient Hybrid Receivers for Millimeter-Wave Massive Multiuser MIMO Uplink Systems With Variable-Resolution ADCs

A primary difficulty in the practical implementation of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is the high hardware complexity and power consumption imposed by the employment of a large number of antennas at the base station. To alleviate this difficulty, we concentrate on a hybrid beamforming architecture consisting of less number of radio-frequency chains with variable-resolution analog-to-digital converters (ADCs) for mmWave massive multiuser MIMO uplink systems. Our objective is to design the ADC resolution profile to maximize system performance while reducing total receiver power consumption. However, the resulting ADC resolution profile design problem incurs high combinatorial complexity and is known to be computationally intractable. We address this problem by devising a simple yet computationally efficient algorithm, namely a coordinate update algorithm, to design the ADC resolution profile. Simulation results show that the proposed algorithm not only provides nearly the same sum rate as full-precision ADC receiver systems but also achieves much higher energy efficiency than existing state-of-the-art receiver architectures.

Quantum version of MMSE-based massive MIMO uplink detection

Regarding the massive multiple-input multiple-output (MIMO) uplink, the quantum version detection based on the minimum mean square error criterion is developed for complexity consideration. Specifically, a comprehensive quantum algorithm for encoding the estimated signal into a quantum state is presented, synthesizing the subroutines of state preparation, Hamiltonian simulation, phase estimation and so on. Indispensable assumptions along with the quantum algorithm are summarized and assumption testing in a MIMO scenario is conducted. Both theoretical analyses from the mathematical point of view and simulated realizations in massive MIMO systems basically confirm the applicability of the quantum algorithm. With desired precision, the quantum algorithm achieves almost quadratic speedup over all classical counterparts. Whereas, with weaker precision accompanied by undetermined performance loss, the quantum algorithm eventually breaks through polynomial-time detection complexity and achieves exponential speedup. Future work will be directed toward the error dependence improvement, classical form outputs and experimental applications in quantum communication systems.

Impact of Stair and Diagonal Matrices in Iterative Linear Massive MIMO Uplink Detectors for 5G Wireless Networks

In massive multiple-input multiple-output (M-MIMO) systems, a detector based on maximum likelihood (ML) algorithm attains optimum performance, but it exhaustively searches all possible solutions, hence, it has a very high complexity and realization is denied. Linear detectors are an alternative solution because of low complexity and simplicity in implementation. Unfortunately, they culminate in a matrix inversion that increases the computational complexity in high loaded systems. Therefore, several iterative methods have been proposed to approximate or avoid the matrix inversion, such as the Neuamnn series (NS), Newton iterations (NI), successive overrelaxation (SOR), Gauss–Siedel (GS), Jacobi (JA), and Richardson (RI) methods. However, a detector based on iterative methods requires a pre-processing and initialization where good initialization impresses the convergence, the performance, and the complexity. Most of the existing iterative linear detectors are using a diagonal matrix ( D ) in initialization because the equalization matrix is almost diagonal. This paper studies the impact of utilizing a stair matrix ( S ) instead of D in initializing the linear M-MIMO uplink (UL) detector. A comparison between iterative linear M-MIMO UL detectors with D and S is presented in performance and computational complexity. Numerical Results show that utilization of S achieves the target performance within few iterations, and, hence, the computational complexity is reduced. A detector based on the GS and S achieved a satisfactory bit-error-rate (BER) with the lowest complexity.

Massive MIMO With Ternary ADCs

Massive multiple-input-multiple-output (MIMO) system inevitably faces the hardware cost and energy efficiency problem due to its large number of antennas at the base station (BS). The use of low-resolution analog-to-digital converters (ADCs), e.g., typical 1-bit ADCs, can effectively reduce the system cost. In this paper, we consider a massive MIMO uplink with ternary/three level ADCs. The design of typical linear combiner based detectors is given along with their analytical symbol-error-rate (SER) performance results. Analytical and simulation results show that 1) ternary ADCs can effectively compensate the SER performance gap between 1-bit and full-resolution ADCs; 2) optimal design of ternary ADCs for SER minimization can be referred to the existing design for quantization error minimization; 3) ternary ADCs perform better than 2-bit ADCs in energy efficiency. Thus, for some low-cost scenarios where implementing 2-bit ADCs for each antenna in massive MIMO may be even unaffordable, ternary ADC can be a good choice.

Joint distance‐based user grouping and pilot assignment schemes for pilot decontamination in massive MIMO systems

SummaryPilot contamination (PC) is one of the most crucial problems in massive multiple‐input multiple‐output (MIMO) systems, due to a limited source of the coherence block. In the current study, two pilot assignment schemes proposed for massive MIMO uplink transmission. We divided each cell into two groups of edge and center users under distance‐based user grouping algorithm (DUG), which the aim is finding the best user grouping boundary. The best boundary is where both signal to interference plus noise Ratio (SINR) of edge and center users is maximum, according to the user's distance to base stations (BS). We investigated the location‐aware pilot assignment (LPA) scheme to enhance the quality of services for both groups. In LPA by assuming three pilot groups for adjacent cells and determining the user's distance to BS, the planned pilots assigned to the users according to their distance to the BS. Then, we optimized pilot schedules in each group by maximizing uplink (UL) rate of each user with auction‐based optimization pilot assignment (AOPA). Simulation results show that both DUG and LPA improve the rate of edge users about 70% and the rate of center users about 30%. Joint DUG and AOPA enhance the rate of the edge and center users about 100% and 80% in each group than conventional schemes and about 50% and 10%, compared with the LPA method. Simulation results show that the proposed method is efficient in reducing PC and improving spectral efficiency.