Ratio Precoding Research Articles

Game Theory-Based Power Allocation Strategy for NOMA in 5G Cooperative Beamforming

Non-orthogonal multiple access (NOMA) is a Fifth Generation (5G) technique that allows many users to simultaneously access the same time–frequency separating channels via successive interference cancellation (SIC) receiver. Cooperative NOMA (CNOMA) is an effective tool to prevent performance degradation of far users by allocating minimal power to users with good channel conditions. In this paper, we proposed a fair power and channel allocation scheme based on the Nash bargaining solution (NBS) game solution in full-duplex, cooperative beamforming (BF) for multicarrier (MC) NOMA. The proposed NBS scheme assigns optimal power and channel allocation according to channel conditions while maintaining a fair rate amongst cooperative users. NBS provides a fair and optimum approach for maximizing the total rate of CNOMA. The signal-to-leakage (SLR) ratio precoding technique is considered as a design performance criterion for beamforming vector towards achieving power domain CNOMA players. Simulation results show that at BER = $${10}^{-5}$$ , the NBS power allocation (proposed scheme) improved by 2 dB in terms of Signal-to-Noise Ratio (SNR), compared with the non-cooperative scheme, and 3 dB compared with the multiple-input multiple-output NOMA (MIMO-NOMA). Both improvements were as a result of interference reduction and information sharing in the network. In terms of fairness, the proposed NBS scheme shown a high level of fairness at 0.8401, compared to the other similar approaches in the literature.

Overview of Precoding Techniques for Massive MIMO

Massive multiple-input multiple-output (MIMO) is playing a crucial role in the fifth generation (5G) and beyond 5G (B5G) communication systems. Unfortunately, the complexity of massive MIMO systems is tremendously increased when a large number of antennas and radio frequency chains (RF) are utilized. Therefore, a plethora of research efforts has been conducted to find the optimal precoding algorithm with lowest complexity. The main aim of this paper is to provide insights on such precoding algorithms to a generalist of wireless communications. The added value of this paper is that the classification of massive MIMO precoding algorithms is provided with easily distinguishable classes of precoding solutions. This paper covers linear precoding algorithms starting with precoders based on approximate matrix inversion methods such as the truncated polynomial expansion (TPE), the Neumann series approximation (NSA), the Newton iteration (NI), and the Chebyshev iteration (CI) algorithms. The paper also presents the fixed-point iteration-based linear precoding algorithms such as the Gauss-Seidel (GS) algorithm, the successive over relaxation (SOR) algorithm, the conjugate gradient (CG) algorithm, and the Jacobi iteration (JI) algorithm. In addition, the paper reviews the direct matrix decomposition based linear precoding algorithms such as the QR decomposition and Cholesky decomposition (CD). The non-linear precoders are also presented which include the dirty-paper coding (DPC), Tomlinson-Harashima (TH), vector perturbation (VP), and lattice reduction aided (LR) algorithms. Due to the necessity to deal with a high consuming power by the base station (BS) with a large number of antennas in massive MIMO systems, a special subsection is included to describe the characteristics of the peak-to-average power ratio precoding (PAPR) algorithms such as the constant envelope (CE) algorithm, approximate message passing (AMP), and quantized precoding (QP) algorithms. This paper also reviews the machine learning role in precoding techniques. Although many precoding techniques are essentially proposed for a small-scale MIMO, they have been exploited in massive MIMO networks. Therefore, this paper presents the application of small-scale MIMO precoding techniques for massive MIMO. This paper demonstrates the precoding schemes in promising multiple antenna technologies such as the cell-free massive MIMO (CF-M-MIMO), beamspace massive MIMO, and intelligent reflecting surfaces (IRSs). In-depth discussion on the pros and cons, performance-complexity profile, and implementation solidity is provided. This paper also provides a discussion on the channel estimation and energy efficiency. This paper also presents potential future directions in massive MIMO precoding algorithms.

Ray-Tracing-Based Numerical Assessment of the Spatiotemporal Duty Cycle of 5G Massive MIMO in an Outdoor Urban Environment

In the near future, wireless coverage will be provided by the base stations equipped with dynamically-controlled massive phased antenna arrays that direct the transmission towards the user. This contribution describes a computational method to estimate realistic maximum power levels produced by such base stations, in terms of the time-averaged normalized antenna array gain. The Ray-Tracing method is used to simulate the electromagnetic field (EMF) propagation in an urban outdoor macro-cell environment model. The model geometry entities are generated stochastically, which allowed generalization of the results through statistical analysis. Multiple modes of the base station operation are compared: from LTE multi-user codebook beamforming to the more advanced Maximum Ratio and Zero-Forcing precoding schemes foreseen to be implemented in the massive Multiple-Input Multiple-Output (MIMO) communication protocols. The influence of the antenna array size, from 4 up to 100 elements, in a square planar arrangement is studied. For a 64-element array, the 95th percentile of the maximum time-averaged array gain amounts to around 20% of the theoretical maximum, using the Maximum Ratio precoding with 5 simultaneously connected users, assuming a 10 s connection duration per user. Connection between the average array gain and actual EMF levels in the environment is drawn and its implications on the human exposure in the next generation networks are discussed.

Massive MIMO enabled joint unicast transmission to IoT devices and mobile terminals

We investigate the viability of downlink (DL) unicast transmission from a base station (BS) with massive number of antennas jointly to support machine-centric communication among the internet-of-things (IoT) devices and human-centric communication among the mobile terminals. Specifically, we derive a new expression for the DL sum spectral efficiency (SE) for the IoT devices with maximum ratio precoding when channel estimates are acquired via the proposed distance-dependent grouping based hybrid pilot assignment strategy. We also derive a new DL sum SE expression for IoT devices based on non-orthogonal pilot assignment. We show that under statistical channel inversion based power control at the BS, the proposed strategy yields the highest sum SE and can serve the largest number of IoT devices when compared against orthogonal, non-orthogonal and distance-independent grouping based hybrid strategies. Furthermore, we also analyse the DL sum SE of mobile terminals in the presence of IoT devices when every mobile is given an orthogonal pilot and prove that the sum SE is independent of the pilot assignment strategy employed by the IoT devices. We also obtain the max–min rate for the IoT devices and prove that it is independent of the pilot assignment strategy used by the devices.

User Selection and Multiuser Widely Linear Precoding for One-Dimensional Signalling

Emergence of ultradense networks in 5G communications, Internet of things, and eHealth devices prompts us to develop new communication techniques that can support a large number of low data rate devices. In particular, it has been shown that when the data are real-valued and the observation is complex-valued, widely linear (WL) estimation can be employed in lieu of linear estimation to improve the performance. With these motivations, we study user selection and transmit precoding in multiuser communication systems assuming transmitted signals are one-dimensionally modulated. A closed-form solution for widely linear maximum signal-to-leakage-and-noise ratio precoding is obtained. We also investigate the design of WL maximum ratio transmission, WL zero-forcing, and WL minimum mean square error precoding techniques. Furthermore, to enable an increased number of communication devices, a user selection algorithm compatible with widely linear processing of one-dimensionally modulated signals is proposed. The proposed user selection algorithm can potentially double the number of simultaneously selected users compared to that of conventional user selection methods.

Per-antenna signal to leakage noise ratio precoding for MU-MIMO-OFDM system with reduced feedback

In this work, multi-user multiple input multiple outputs orthogonal frequency division multiplexing (MU-MIMO-OFDM) precoding is introduced. The proposed method makes use of per-antenna signal to leakage plus noise ratio (PA-SLNR) as optimisation criteria. This optimisation criterion takes both the inter-user and inter-antennas interference cancellation into account. Unlike the conventional proposed methods in which simple frequency flat fading is assumed, in the proposed method we generalised the precoding problem to more realistic frequency selective MU-MIMOOFDM Broadcast channel where OFDM is used to mitigate inter-symbol-interference (ISI) and the MU-MIMO is used for spatial multiplexing. Conventionally, each individual user has to feedback the channel frequency response of each subcarrier to the central node base station for precoding matrix design which leads to high traffic load in the feedback channel. In this work we also introduce a simple frequency response channel factorisation such that each user is required to feedback only one subcarrier time domain channel impulse response which dramatically reduces the amount of the feedback load.

Per-antenna signal to leakage noise ratio precoding for MU-MIMO-OFDM system with reduced feedback

In this work, multi-user multiple input multiple outputs orthogonal frequency division multiplexing (MU-MIMO-OFDM) precoding is introduced. The proposed method makes use of per-antenna signal to leakage plus noise ratio (PA-SLNR) as optimisation criteria. This optimisation criterion takes both the inter-user and inter-antennas interference cancellation into account. Unlike the conventional proposed methods in which simple frequency flat fading is assumed, in the proposed method we generalised the precoding problem to more realistic frequency selective MU-MIMOOFDM Broadcast channel where OFDM is used to mitigate inter-symbol-interference (ISI) and the MU-MIMO is used for spatial multiplexing. Conventionally, each individual user has to feedback the channel frequency response of each subcarrier to the central node base station for precoding matrix design which leads to high traffic load in the feedback channel. In this work we also introduce a simple frequency response channel factorisation such that each user is required to feedback only one subcarrier time domain channel impulse response which dramatically reduces the amount of the feedback load.

Coordinated beamforming schemes based on modified signal‐to‐leakage‐plus‐noise ratio precoding designs

In this study, linear transceiver designs are proposed based on modified definitions of signal-to-leakage-plus-noise ratio (mSLNR) for multi-cell coordinated beamforming scenarios. Two mSLNR definitions are presented, one of which modifies the computation of leakage and noise powers, while the other applies the modified procedure to the desired power calculation in addition to the computation of leakage and noise powers. The proposed mSLNRs are generally valid for any receiver types. Various linear receivers are studied, several of which, when applied with the first mSLNR definition, are shown to be equivalent. Moreover, a special case of the proposed scheme is shown to be equivalent to an existing algorithm. Iterative precoding algorithms are also presented with a discussion on their convergence properties. The proposed transceiver designs are further extended to incorporate scheduling functionalities. A weighted sum rate maximisation problem is formulated and is solved by two separate approaches, namely power allocation and data-substream selection methods. Simulation results show that, for multiple users per cell, the proposed algorithms can effectively integrate user and substream selections and achieve multi-user diversity gain.

Double-Cap Differential Codebook Structure for MU MISO Systems in Correlated Channels

We propose a double-cap differential codebook (DCB) design for multiuser (MU) multiple-input-single-output systems. The MU transmission uses signal-to-leakage-and-noise ratio precoding. The double-cap DCB is designed for temporally/ spatially correlated channels and is based on a combined spherical and polar cap structure. We consider an inner spherical cap and an outer polar cap structure with different radii. The design allows us to systematically alter the radii of the caps over the feedback time depending on the previously selected codeword. The proposed double-cap DCB does not require knowledge of temporal or spatial channel correlation at the base-station, thus removing the overhead of additional feedback from the user. Simulation results show the effectiveness of the proposed double-cap DCB over a single polar cap DCB in terms of sum capacity gain and quantization errors.