Constant envelope precoding by interference exploitation in phase shift keying-modulated multiuser transmission

We introduce a new approach to constant-envelope precoding (CEP) based on an interference-driven optimization region for generic phase-shift-keying modulations in the multi-user (MU) multiple-input-multiple-output downlink. While conventional precoding approaches aim to minimize the multi-user interference (MUI) with a total sum-power constraint at the transmitter, in the proposed scheme we consider MUI as a source of additional energy to increase the signal-to-interference-and-noise-ratio at the receiver. In our studies, we focus on two different CEP approaches: a first technique, where the power at each antenna is fixed to a specific value, and a two-step approach, where we first relax the power constraints to be lower than a defined parameter and then enforce CEP transmission. The algorithms are studied in terms of computational costs, with a detailed comparison between the proposed approach and the classical interference suppression schemes from the literature. Moreover, we analytically derive a robust optimization region to counteract the effects of channel-state estimation errors. The presented schemes are evaluated in terms of achievable symbol error rate in a perfect and imperfect channel-state information scenario for different modulation orders. Our results show that the proposed techniques further extend the benefits of classical CEP by judiciously relaxing the optimization region

On the Finite Constellation Sum Rates for ZF and CI Precoding

This paper analyzes the performance of multi-user multiple-input multiple-output (MU-MIMO) systems, with a finite phase-shift keying (PSK) input alphabet. The achievable sum rate is investigated for two precoding techniques, namely: 1) zero forcing (ZF) precoding, 2) constructive interference (CI) precoding. In light of this, new analytical expressions for the average sum rate are derived in the two scenarios, and Monte Carlo simulations are provided throughout to confirm the analysis. Furthermore, based on the derived expressions, a power allocation scheme that can ensure fairness among the users is also investigated. The results in this paper demonstrate that, the CI strictly outperforms the ZF scheme, and the performance gap between the considered schemes depends essentially on the system parameters

Diversity Order Analysis for Quantized Constant Envelope Transmission

Quantized constant envelope (QCE) transmission is a popular and effective technique to reduce the hardware cost and improve the power efficiency of 5G and beyond systems equipped with large antenna arrays. It has been widely observed that the number of quantization levels has a substantial impact on the system performance. This paper aims to quantify the impact of the number of quantization levels on the system performance. Specifically, we consider a downlink single-user multiple-input-single-output (MISO) system with M-phase shift keying (PSK) constellation under the Rayleigh fading channel. We first derive a novel bound on the system symbol error probability (SEP). Based on the derived SEP bound, we characterize the achievable diversity order of the quantized matched filter (MF) precoding strategy. Our results show that full diversity order can be achieved when the number of quantization levels L is greater than the PSK constellation order M, i.e., L>M, only half diversity order is achievable when L=M, and the achievable diversity order is 0 when L<M. Simulation results verify our theoretical analysis.Comment: 9 pages, 3 figures, submitted for possible publicatio

A Mixed-Integer Programming Approach to Interference Exploitation for Massive-MIMO

A novel low-complexity transmission scheme for Massive Multiuser Multi-Input Multi-Output (M-MU-MIMO) is proposed, where Transmit Antenna Selection (TAS) and beam-forming are jointly performed to exploit multiuser interference. Two separate solutions to the deriving optimization problem are proposed: a mixed-integer programming approach that can optimally solve the TAS-beamforming problem and a heuristic convex approach, based on the assumption of matched filtering beamforming. Numerical results prove that the proposed multiuser interference exploiting approaches are able to greatly outperform previous state-of-the-art schemes, where TAS and beamforming are disjointedly solved

Constructive Interference Based Constant Envelope Precoding

We present a new multiple-input-multiple-output (MIMO) transmission scheme for generic phase-shift-keying (PSK) modulations in the multi-user (MU) downlink channel, where Constant Envelope Precoding (CEP) is combined with concepts of interference exploitation. In the proposed approach, multi-user-interference (MUI) is treated as a resource for increasing the signal-to-interference-and-noise-ratio (SINR) at the receiver side, in contrast with conventional precoding schemes from the literature which aim to minimize MUI. Two different CEP schemes are presented: a first technique, based on the application of the cross-entropy solver, and a two-step approach, based on an initial relaxation of the power constraints and a subsequent enforcement of per-antenna power constraints. The benefits of the proposed algorithms are evaluated in terms of computational costs and achievable symbol error rate (SER) in a perfect channel state information (CSI) scenario for different modulation orders. The analytical and numerical results show that interference-exploitation concepts are able to further extend the benefits of classical CEP

Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-art, Classification and Challenges

Precoding has been conventionally considered as an effective means of mitigating or exploiting the interference in the multiantenna downlink channel, where multiple users are simultaneously served with independent information over the same channel resources. The early works in this area were focused on transmitting an individual information stream to each user by constructing weighted linear combinations of symbol blocks (codewords). However, more recent works have moved beyond this traditional view by: i) transmitting distinct data streams to groups of users and ii) applying precoding on a symbol-per-symbol basis. In this context, the current survey presents a unified view and classification of precoding techniques with respect to two main axes: i) the switching rate of the precoding weights, leading to the classes of block-level and symbol-level precoding, ii) the number of users that each stream is addressed to, hence unicast, multicast, and broadcast precoding. Furthermore, the classified techniques are compared through representative numerical results to demonstrate their relative performance and uncover fundamental insights. Finally, a list of open theoretical problems and practical challenges are presented to inspire further research in this area

Energy Efficient Large Scale Antenna Systems for 5G Communications and Beyond

The increasing popularity of mobile devices has fueled an exponential growth in data traffic. This phenomenon has led to the development of systems that achieve higher spectral efficiencies, at the cost of higher power consumptions. Consequently, the investigation on solutions that allow to increase the maximum throughput together with the energy efficiency becomes crucial for modern wireless systems. This thesis aims to improve the trade-off between performances and power consumption with special focus toward multiuser multiple-antenna communications, due to their promising benefits in terms of spectral efficiency. Research envisaged massive Multi-Input-Multi-Output (MIMO) systems as the main technology to meet these data traffic demands, as very large arrays lead to unprecedented data throughputs and beamforming gains. However, larger arrays lead to increased power consumption and hardware complexity, as each radiating element requires a radio frequency chain, which is accountable for the highest percentage of the total power consumption. Nonetheless, the availability of a large number of antennas unveils the possibility to wisely select a subset of radiating elements. This thesis shows that multiuser interference can be exploited to increase the received power, with significant circuit power savings at the base station. Similarly, millimeter-wave communications experienced raising interest among the scientific community because of their multi-GHz bandwidth and their ability to place large arrays in limited physical spaces. Millimeter-wave systems inherit same benefits and weaknesses of massive MIMO communications. However, antenna selection is not viable in millimeter-wave communications because they rely on high beamforming gains. Therefore, this thesis proposes a scheme that is able to reduce the number of radio frequency chains required, while achieving close-to-optimal performances. Analytical and numerical results show that the proposed techniques are able to improve the overall energy efficiency with respect to the state-of-the-art, hence proving to be valid candidates for practical implementations of modern communication systems

A Memory-Efficient Learning Framework for Symbol Level Precoding with Quantized NN Weights

This paper proposes a memory-efficient deep neural network (DNN) framework-based symbol level precoding (SLP). We focus on a DNN with realistic finite precision weights and adopt an unsupervised deep learning (DL) based SLP model (SLP-DNet). We apply a stochastic quantization (SQ) technique to obtain its corresponding quantized version called SLP-SQDNet. The proposed scheme offers a scalable performance vs memory trade-off, by quantizing a scalable percentage of the DNN weights, and we explore binary and ternary quantizations. Our results show that while SLP-DNet provides near-optimal performance, its quantized versions through SQ yield ~3.46&#x00D7; and ~2.64&#x00D7; model compression for binary-based and ternary-based SLP-SQDNets, respectively. We also find that our proposals offer ~20&#x00D7; and ~10&#x00D7; computational complexity reductions compared to SLP optimization-based and SLP-DNet, respectively

Near-Optimal Interference Exploitation 1-Bit Massive MIMO Precoding via Partial Branch-and-Bound

In this paper, we focus on 1-bit precoding for large-scale antenna systems in the downlink based on the concept of constructive interference (CI). By formulating the optimization problem that aims to maximize the CI effect subject to the 1-bit constraint on the transmit signals, we mathematically prove that, when relaxing the 1-bit constraint, the majority of the obtained transmit signals already satisfy the 1-bit constraint. Based on this important observation, we propose a 1-bit precoding method via a partial branch-and-bound (P-BB) approach, where the BB procedure is only performed for the entries that do not comply with the 1-bit constraint. The proposed P-BB enables the use of the BB framework in large-scale antenna scenarios, which was not applicable due to its prohibitive complexity. Numerical results demonstrate a near-optimal error rate performance for the proposed 1-bit precoding algorithm.Comment: accepted by IEEE ICASSP202