mMIMO Systems Research Articles

Statistical CSI-Based Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems With Direct Links

This letter investigates the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (mMIMO) systems with direct links, and the phase shifts of the RIS are designed based on the statistical channel state information (CSI). We first derive the closed-form expression of the uplink ergodic data rate. Then, based on the derived expression, we use the genetic algorithm (GA) to solve the sum data rate maximization problem. With low-complexity maximal-ratio combination (MRC) and low-overhead statistical CSI-based scheme, we validate that the RIS can bring significant performance gains to the traditional mMIMO systems.

Sum Rate Maximization Versus MSE Minimization in FDD Massive MIMO Systems With Short Coherence Time

The increasing demand for higher data rates motivates the exploration of advanced techniques for future wireless networks. To this end, massive multiple-input multiple-output (mMIMO) is envisioned as the most essential technique to meet this demand. However, the expansion of the number of antennas in mMIMO systems with short coherence time makes the downlink channel estimation (DCE) overhead potentially overwhelming. As such, the number of training sequence (TS) needs to be significantly reduced. However, reducing the number of TS reduces the mean-squared error (MSE) accuracy significantly and to date it is not clear to what extend can this TS reduction affects the achievable sum rate performance. Therefore, this paper develops a low complexity and tractable TS solution for DCE and establishes an analytical framework for the optimum TS. Furthermore, the tradeoff between the achievable sum rate maximization criteria and the MSE minimization criteria is investigated. This investigation is essential to characterize the optimum TS length and the actual performance of mMIMO systems when the channel exhibits a limited coherence time. To this end, the statistical structure of mMIMO channels is exploited. In addition, this paper utilizes a random matrix theory (RMT) method to characterize the downlink achievable sum rate and MSE in a closed-form. This paper shows that maximizing the downlink sum rate criterion is more important than minimizing the MSE of the SINR only, which is typically considered in the conventional MIMO systems and/or in the time division duplex (TDD) mMIMO systems. The results demonstrate that a feasible downlink achievable sum rate can be achieved in an frequency division duplex (FDD) mMIMO system. This finding is necessary to extend the benefit of mMIMO systems to high frequency bands such as millimeter-wave (mmWave) and Terahertz (THZ) communications.

Uplink Signal Detection for Scalable Cell-Free Massive MIMO Systems With Robustness to Rate-Limited Fronthaul

We consider the problem of uplink signal detection in scalable cell-free mMIMO (CF-mMIMO) systems subject to limited fronthaul link capacity and highly correlated channel conditions. Unlike centralized MIMO systems, in which all receive antennas are placed at a central access point (CAP), in the CF-mMIMO architecture the CAP serving a given area also uses information ( i.e. channel estimates and receive signals) collected by a set of surrounding access points (APs). For such a scenario, two new robust receivers are designed, which can combat the effects of limited fronthaul capacity by leveraging knowledge of the heteroscedastic covariance of the resulting effective noise. The first receiver, which has a higher complexity but yields the best performance, is based on an expectation propagation (EP) approach, while the second employs the effective noise heteroscedastic covariance in a generalized least squares (GLS) variation of the maximum likelihood (ML) detection problem. Simulation results confirm the efficacy of both proposed receivers, which are further employed to empirically study the optimum distribution of antennas among the CAP and APs.

Sum-Rate of Cell Free Massive MIMO Systems With Power Amplifier Non-Linearity

Cell free (CF) massive multiple input multiple output (mMIMO) has been suggested as a key solution to meet the high data rate demands of future wireless communications. Studying the performance of these systems in practical scenarios such as in the presence of hardware impairments is of vital importance. In this paper, we study the effect of power amplifier non-linearity on the uplink and downlink sum-rate of CF mMIMO systems. We derive closed form expressions for the uplink and downlink achievable sum-rates of orthogonal frequency division multiplexing (OFDM) based CF mMIMO systems. Our results show that in the uplink the sum-rate does not increase unlimitedly as the number of access points (AP) increases, being upper bounded, contrarily to the ideal linear case. In fact, the rate of each user is limited by the distortion signal of its power amplifier. However, in the downlink, the sum-rate of the system with non-linear power amplifiers is not bounded and increases unlimitedly with the number of APs. Our results also indicate that for the same signal to distortion ratio (SDR) at the power amplifier output or the same normalized saturation level of the power amplifiers, the relative downlink sum-rate degradation is lower than the relative uplink sum-rate degradation (both with respect to their corresponding values in the ideal linear case). In fact, our results confirm that the user side power amplifier non-linearity has higher impact on the system performance than the power amplifier non-linearity on the AP antennas.

Multipair Two-Way DF Relaying With Cell-Free Massive MIMO

We consider a two-way half-duplex decode-and-forward (DF) relaying system with multiple pairs of single-antenna users assisted by a cell-free (CF) massive multiple-input multiple-output (mMIMO) architecture with multiple-antenna access points (APs). Under the practical constraint of imperfect channel state information (CSI), we derive the achievable sum spectral efficiency (SE) for a finite number of APs with maximum ratio (MR) linear processing for both reception and transmission in closed-form. Notably, the proposed CF mMIMO relaying architecture, exploiting the spatial diversity, and providing better coverage, outperforms the conventional collocated mMIMO deployment. Moreover, we shed light on the power-scaling laws maintaining a specific SE as the number of APs grows. A thorough examination of the interplay between the transmit powers per pilot symbol and user/APs takes place, and useful conclusions are extracted. Finally, differently to the common approach for power control in CF mMIMO systems, we design a power allocation scheme maximizing the sum SE.

Cell-Free Massive MIMO System With an Adaptive Switching Algorithm Between Cooperative NOMA, Non-Cooperative NOMA, and OMA Modes

Non-orthogonal multiple access (NOMA) aided cell-free massive multiple-input multiple-output (CF mMIMO) systems have gained great attention over the past few years to be employed in the sixth generation of the wireless network. With distributed antennas over a large area, CF mMIMO systems have much better cell coverage and higher achievable sum-rate compared to the conventional cellular and small-cell networks. This paper will propose a novel cooperative NOMA-aided CF mMIMO system. The proposed scheme will increase the achievable sum-rate and improve the system reliability by creating additional data streams between users within the same NOMA cluster. Also, we will suggest an adaptive switching algorithm between non-cooperative NOMA, cooperative NOMA, and orthogonal multiple access (OMA) modes in order to maximize the power efficiency and achievable sum-rate. We will derive closed-form expressions for the achievable downlink data rates of the mentioned modes. Lastly, we will offer a special user clustering algorithm that needs no complex optimization and ensures the existence of reliable cooperative links between the users. The simulation results will prove the superiority of the proposed scheme over the conventional NOMA-based and OMA-based CF mMIMO systems.

A Dual-Functional Massive MIMO OFDM Communication and Radar Transmitter Architecture

In this study, a dual-functional radar and communication (RadCom) system architecture is proposed for application at base-stations (BSs), or access points (APs), for simultaneously communicating with multiple user equipments (UEs) and sensing the environment. Specifically, massive multiple-input multiple-output (mMIMO) communication and orthogonal frequency-division multiplexing (OFDM)-based MIMO radar are considered with the objective to jointly utilize channel diversity and interference. The BS consists of a mMIMO antenna array, and radar transmit and receive antennas. Employing OFDM waveforms for the radar allows the BS to perform channel state information (CSI) estimation for the mMIMO and radar antennas simultaneously. The acquired CSI is then exploited to predict the radar signals received by the UEs. While the radar transmits an OFDM waveform for detecting possible targets in range, the communication system beamforms to the UEs by taking into account the predicted radar interference. To further enhance the capacity of the communication system, an optimum radar waveform is designed. Moreover, the network capacity is mathematically analyzed and verified by simulations. The results show that the proposed RadCom can achieve higher capacity than conventional mMIMO systems by utilizing the radar interference while simultaneously detecting targets.

Enhancement of Efficiency and Performance Gain of Massive MIMO System Using Trial-Based Rider Optimization Algorithm

Massive multiple-input multiple output (mMIMO) is considered as one of the most in demand and innovative technologies for the fifth-generation wireless communication systems. This paper attempts to frame a mMIMO system model, intends to improve the spectral efficiency, energy efficiency and performance gain. Here, the system performance achievements are premeditated in a multi-cell downlink mMIMO system under the core considerations such as “imperfect channel estimation, perfect channel estimation and the effect of interference among cells due to pilot sequences contamination”. The performance gains such as “spatial multiplexing gain, array gain and spatial diversity gain” are considered to maximize in this paper. For attaining this multi-objective function, an improved meta-heuristic algorithm called rider optimization algorithm (ROA) known as trial-based ROA adopted, and analyse the performance of the proposed model by comparing over existing models.

Investigation of low-density parity check codes concatenated multi-user massive multiple-input multiple-output systems with imperfect channel state information

In this era of communication technology, it is desirable to increase the data rate while minimizing the error to improve the system’s reliability. One of these techniques is massive multiple-input multiple-output (mMIMO), which increases the spectral efficiency by providing the data to multiple users simultaneously through spatial multiplexing. The mMIMO system processes the received signal by prior estimation of the channel, which has a finite variance leading to imperfect channel state information (ICSI) at the receiver. In the fifth-generation technology, spectral efficiency using mMIMO may decrease as the number of subscribers increases, resulting in more interference and affecting system capacity. The ICSI provides another challenge, as the processed data at the receiver’s output may now be more erroneous. Thus, this article provides an insight into the impact of an increase in the number of users on the variation in bit error rate with the signal-to-noise ratio in multi-user mMIMO (MU-mMIMO) and low-density parity check (LDPC) codes concatenated MU-mMIMO systems having ICSI at the receiver for quadrature amplitude modulation (QAM) and 16-QAM as modulation techniques. It has been shown that the performance of the concatenated scheme outperforms the conventional mMIMO system.

Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems

Both the power-dissipation and cost of massive multiple-input multiple-output (mMIMO) systems may be substantially reduced by using low-resolution analog-to-digital converters (LADCs) at the receivers. However, both the coarse quantization of LADCs and the inaccurate instantaneous channel state information (ICSI) degrade the performance of quantized mMIMO systems. To overcome these challenges, we propose a novel stochastic hybrid analog-digital combiner (SHC) scheme for adapting the hybrid combiner to the long-term statistics of the channel state information (SCSI). We seek to minimize the transmit power by jointly optimizing the SHC subject to average rate constraints. For the sake of solving the resultant nonconvex stochastic optimization problem, we develop a relaxed stochastic successive convex approximation (RSSCA) algorithm. Simulations are carried out to confirm the benefits of our proposed scheme over the benchmarkers.

Upper Bound on Side Lobe Levels for mMIMO Antennas to Evaluate the Beam Steering Capability

A promising concept to meet the growing requirements of future wireless systems is the use of massive multiple-input-multiple-output (mMIMO) systems. Specifically, in an attempt to mitigate the high cost due to the complexity of these systems, sub-array based antenna architectures are highly anticipated, where not every antenna element is independently controllable, but groups of elements are connected. Consequently, sharing the steering circuitry reduces degrees of freedom in the design of effective beam patterns. Due to this, sub-array based antenna systems suffer from severely increased side lobe levels (SLLs), which limit the steering capability of mMIMO systems. Therefore, it is difficult to establish a distinct general optimum tool for beam pattern design. In this letter, we explore theoretically a mechanism to describe SLLs due to beam steering in uniform sub-array based mMIMO systems, and describe an analytical upper bound on the SLL distance as a function of the steering angle. The derived expression can be used to evaluate the steering capabilities of sub-array based configuration.

A Review of Massive Multiple Input Multiple Output for 5G Communication: Benefits and Challenges

Massive MIMO (mMIMO) systems become a primary advantage to overcome the problem of bandwidth restrictions. It improves the channel capacity of remote systems.The paper reviews about mMIMO systems. mMIMO consists of several number of antennas at base station (BS) which improves spectrum efficacy. The extra benefit of the mMIMO system is that the components cost is low because of utilization of less power components. The paper also discusses about the channel estimation at the BS and generally time division mode (TDD) is assumed for mMIMO systems. The paper also discusses system model, benefits for 5G wireless communication and its challenges.

A Review of Massive Multiple Input Multiple Output for 5G Communication: Benefits and Challenges

Massive MIMO (mMIMO) systems become a primary advantage to overcome the problem of bandwidth restrictions. It improves the channel capacity of remote systems.The paper reviews about mMIMO systems. mMIMO consists of several number of antennas at base station (BS) which improves spectrum efficacy. The extra benefit of the mMIMO system is that the components cost is low because of utilization of less power components. The paper also discusses about the channel estimation at the BS and generally time division mode (TDD) is assumed for mMIMO systems. The paper also discusses system model, benefits for 5G wireless communication and its challenges.

Triangular Non-Orthogonal Random Access in mMIMO Systems

Massive multiple-input multiple-output (mMIMO) based grant-free random access (RA) (mGFRA) has been studied for massive machine-type communication (mMTC). In the existing works of mGFRA, it is implicitly assumed that each RA user equipment (UE) in mMTC has the same data-packet size. However, this may not be the case in practice as RA UEs in different applications can have different numbers of bits to send, which may result in data packets of different sizes. In addition to that, preamble collision is a key issue that degrades the performance of mGFRA when orthogonal preamble is used. In this article, we aim to address the preamble collision issue under a practical mGFRA scenario that consists of two different types of RA UEs (with short and large data packets). Specifically, by exploiting different sizes of data packets, we propose a triangular non-orthogonal RA (T-NORA) scheme, where the preamble transmission of RA UEs with small-sized data packet is embedded into the data transmission of RA UEs with large-sized data packet so that the preamble collision between two different types of RA UEs can be avoided. As a result, an improved performance over conventional mGFRA can be achieved by T-NORA with the assistance of mMIMO. Through analytical and simulation results, we can confirm that the proposed T-NORA scheme outperforms the conventional mGFRA scheme.

Performance of Data-Rate Analysis of Massive MIMO System Using User Grouping

The major challenges in massive MIMO networks are to increase the system throughput and capacity with low complexity and reliability of the wireless communication system. The computational complexity and system capacity of massive MIMO (M-MIMO) systems depend on adequate user grouping, user selection, and antenna scheduling methods. Additionally, the interference of the M-MIMO system is reduced when used suitable signal processing schemes. In this paper, we proposed a novel circular user grouping technique to reduce system complexity. Later, semi-orthogonal user selection with three different combinations of user selection schemes and norm-based antennas scheduling method is used to achieve higher system capacity in M-MIMO systems. Moreover, simple linear zero-forcing and minimum mean square error signal processing schemes are used to minimize the user’s interference. The simulation results show that our proposed algorithm provides very low computational complexity, reduce the interference and higher the system capacity compare to the traditional M-MIMO system.

Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems

Although millimeter-wave (mmWave) and massive multiple input multiple output (mMIMO) can be considered as promising technologies for future mobile communications (beyond 5G or 6G), some hardware limitations limit their applicability. The hybrid analog-digital architecture has been introduced as a possible solution to avoid such issues. In this paper, we propose a two-step hybrid multi-user (MU) equalizer combined with low complexity hybrid precoder for wideband mmWave mMIMO systems, as well as a semi-analytical approach to evaluate its performance. The new digital non-orthogonal multi carrier modulation scheme generalized frequency division multiplexing (GFDM) is considered owing to its efficient performance in terms of achieving higher spectral efficiency, better control of out-of-band (OOB) emissions, and lower peak to average power ratio (PAPR) when compared with the orthogonal frequency division multiplexing (OFDM) access technique. First, a low complexity analog precoder is applied on the transmitter side. Then, at the base station (BS), the analog coefficients of the hybrid equalizer are obtained by minimizing the mean square error (MSE) between the hybrid approach and the full digital counterpart. For the digital part, zero-forcing (ZF) is used to cancel the MU interference not mitigated by the analog part. The performance results show that the performance gap of the proposed hybrid scheme to the full digital counterpart reduces as the number of radio frequency (RF) chains increases. Moreover, the theoretical curves almost overlap with the simulated ones, which show that the semi-analytical approach is quite accurate.

A Cooperative Massive MIMO System for Future In Vivo Nanonetworks

Terahertz propagation inside human body tissues suffers from a low achievable capacity, which drastically limits the usefulness of in vivo nanoscale networks. In this article, a virtual massive multiple-input multiple-output (m-MIMO) array architecture constructed by the cooperative and opportunistic action of distributed nano nodes to form a cluster is introduced for the first time for in vivo nano communications. Moreover, a joint carefully designed scheme merging an antenna-selection-based maximum magnitude (MM) criterion and a spatial modulation (SM) transmission concept is incorporated within this virtual m-MIMO architecture, as a way to achieve the best compromise between performance and complexity. Capacity maximization achieved by the proposed MM antenna selection is also derived for the proposed system. Simulation results consolidate the effectiveness of the proposed MM antenna-selection-assisted SM for the virtual m-MIMO system inside human bodies. It is shown that the MM antenna selection can greatly improve the performance of the proposed architecture in terms of the bit error rate and reduce the complexity.

Two dimension angle of arrival-based precoding for pilot contamination reduction in multi-cell massive MIMO systems

This paper proposes a new precoding approach that utilizes two-dimension (2D) Angle of Arrival (AOA) based precoding (beamforming) approach for multi-cell massive Multiple Input Multiple Output (m-MIMO) systems. The proposed approach enhances the performance of m-MIMO systems by overcoming pilot contamination that is caused by corrupted Channel State Information. It exploits the spatial properties of the propagated signals to distinguish between various users in the multi-cell m-MIMO system and selects the partial space for beamforming based on AOA information to suppress inter-cell and intra-cell interference. We mathematically analyze the impact of pilot contamination in the uplink training and determine the closed-form approximation of uplink Minimum Mean Square Error (MMSE) estimator. By using MMSE estimator as well as 2D AOA and channel estimation, a corresponding MMSE precoding which minimizes the effect of pilot contamination and improves the downlink achievable rate of the system is proposed. Furthermore, a comparison is made of the resolution between the various 2D AOA estimations for an m-MIMO and the optimum values closest bound for the proposed precoding was chosen to reduce pilot contamination. The performance of the proposed MMSE precoding based on 2D AOA estimation methods: 2D Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (2D UESPRIT), 2D Fourier Domain Line Search MUSIC (2D FDLSM), and 2D Propagator Method (2D PM)) were evaluated and compared with pilot contaminated system and deterministic MMSE precoding in terms of their respective achievable rate. Simulation results reveal that the achievable sum rate gains of 2D UESPRIT, 2D FDLSM, and 2D PM based precoding which achieved 97.6%, 92.4%, 86.3% of the desired deterministic performance, respectively, compared to pilot contaminated system, which achieved only 90% even when in the best case of having only one contaminated user out of all users within the activated cell. Findings from our research indicate the feasibility of utilizing the AOA based beamforming to reduce pilot contamination effect that is inherent in a larger order m-MIMO system for the Fifth Generation (5G) wireless transmissions.

Optimal Channel Equalizer for mmWave Massive MIMO Using 1-bit ADCs in Frequency-Selective Channels

Millimeter wave (mmWave) massive multiple-input and multiple-output (mMIMO) is a key technology for increasing the capacity of the future wireless communication systems. However, high-resolution ADCs are costly and infeasible for mmWave mMIMO systems due to high power consumption. Hence, there is a lot of interest in low-resolution ADCs including ultra-low-precision 1-bit ADCs. Such ADCs can be implemented with very low power consumption and hardware cost. However, new channel equalization algorithms are required to compensate the severe nonlinear distortion caused by such low-resolution ADCs. In this work, we analytically derive the maximum likelihood sequence estimation (MLSE) channel equalizer for MIMO systems in frequency-selective fading channels using 1-bit ADCs.

Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations

In the Fifth Generation of telecommunications networks (5G), it is possible to use massive Multiple Input Multiple Output (MIMO) systems, which require efficient receivers capable of reaching good performance values. MIMO systems can also be extended to massive MIMO (mMIMO) systems, while maintaining their, sometimes exceptional, performance. However, we must be aware that this implies an increase in the receiver complexity. Therefore, the use of mMIMO in 5G and future generations of mobile receivers will only be feasible if they use very efficient algorithms, so as to maintain their excellent performance, while coping with increasing and critical user demands. Having this in mind, this paper presents and compares three types of receivers used in MIMO systems, for further use with mMIMO systems, which use Single-Carrier with Frequency-Domain Equalization (SC-FDE), Iterative Block Decision Feedback Equalization (IB-DFE) and Maximum Ratio Combining (MRC) techniques. This paper presents and compares the theoretical and simulated performance values for these receivers in terms of their Bit Error Rate (BER) and correlation factor. While one of the receivers studied in this paper achieves a BER performance nearly matching the Matched Filter Bound (MFB), the other receivers (IB-DFE and MRC) are more than 1 dB away from MFB. The results obtained in this paper can help the development of ongoing research involving hybrid analog/digital receivers for 5G and future generations of mobile communications.