Insights and approaches for low-complexity 5G small-cell base-station design for indoor dense networks

This paper investigates low-complexity approaches to small-cell base-station (SBS) design, suitable for future 5G millimeter-wave (mmWave) indoor deployments. Using large-scale antenna systems and high-bandwidth spectrum, such SBS can theoretically achieve the anticipated future data bandwidth demand of 10000 fold in the next 20 years. We look to exploit small cell distances to simplify SBS design, particularly considering dense indoor installations. We compare theoretical results, based on a link budget analysis, with the system simulation of a densely deployed indoor network using appropriate mmWave channel propagation conditions. The frequency diverse bands of 28 and 72 GHz of the mmWave spectrum are assumed in the analysis. We investigate the performance of low-complexity approaches using a minimal number of antennas at the base station and the user equipment. Using the appropriate power consumption models and the state-of-the-art sub-component power usage, we determine the total power consumption and the energy efficiency of such systems. With mmWave being typified nonline-of-sight communication, we further investigate and propose the use of direct sequence spread spectrum as a means to overcome this, and discuss the use of multipath detection and combining as a suitable mechanism to maximize link reliability

Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In 5G and beyond radios, the increased bandwidth, the fast-changing waveform scenarios, and the operation of large array multiple-input multiple-output (MIMO) transmitter architectures have challenged both the polynomial and the artificial neural network (ANN) MIMO adaptive digital predistortion (DPD) schemes. This article proposes training data selection methods and dimensionality reduction techniques that can be combined to enable relevant reductions of the DPD training time and the implementation complexity for MIMO transmitter architectures. In this work, the combination of an efficient uncorrelated equation selection (UES) mechanism together with orthogonal least squares (OLS) is proposed to reduce the training data length and the number of basis functions at every behavioral modeling matrix in the polynomial MIMO DPD scheme. For ANN MIMO DPD architectures, applying UES and principal component analysis (PCA) is proposed to reduce the input dataset length and features, respectively. The UES-OLS and the UES-PCA techniques are experimentally validated for a 2×2 MIMO test setup with strong power amplifier (PA) input and output crosstalk.This work was supported in part by the MCIN/AEI/10.13039/501100011033 under Project PID2020-113832RB-C22 and Project PID2020-113832RB-C21; and in part by the European Union-NextGenerationEU through the Spanish Recovery, Transformation and Resilience Plan, under Project TSI-063000-2021-121 (MINECO UNICO Programme).Peer ReviewedPostprint (author's final draft

Deep Learning Designs for Physical Layer Communications

Wireless communication systems and their underlying technologies have undergone unprecedented advances over the last two decades to assuage the ever-increasing demands for various applications and emerging technologies. However, the traditional signal processing schemes and algorithms for wireless communications cannot handle the upsurging complexity associated with fifth-generation (5G) and beyond communication systems due to network expansion, new emerging technologies, high data rate, and the ever-increasing demands for low latency. This thesis extends the traditional downlink transmission schemes to deep learning-based precoding and detection techniques that are hardware-efficient and of lower complexity than the current state-of-the-art. The thesis focuses on: precoding/beamforming in massive multiple-inputs-multiple-outputs (MIMO), signal detection and lightweight neural network (NN) architectures for precoder and decoder designs. We introduce a learning-based precoder design via constructive interference (CI) that performs the precoding on a symbol-by-symbol basis. Instead of conventionally training a NN without considering the specifics of the optimisation objective, we unfold a power minimisation symbol level precoding (SLP) formulation based on the interior-point-method (IPM) proximal ‘log’ barrier function. Furthermore, we propose a concept of NN compression, where the weights are quantised to lower numerical precision formats based on binary and ternary quantisations. We further introduce a stochastic quantisation technique, where parts of the NN weight matrix are quantised while the remaining is not. Finally, we propose a systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. Furthermore, we investigate performance complexity tradeoffs via regularisation constraints on the layer weights such that, at inference, parts of network layers can be removed with minimal impact on the detection accuracy. Simulation results show that our proposed learning-based techniques offer better complexity-vs-BER (bit-error-rate) and complexity-vs-transmit power performances compared to the state-of-the-art MIMO detection and precoding techniques

Location-Based Beamforming and Physical Layer Security in Rician Wiretap Channels

We propose a new location-based beamforming (LBB) scheme for wiretap channels, where a multi-antenna source communicates with a single-antenna legitimate receiver in the presence of a multi-antenna eavesdropper. We assume that all channels are in a Rician fading environment, the channel state information from the legitimate receiver is perfectly known at the source, and that the only information on the eavesdropper available at the source is her location. We first describe how the optimal beamforming vector that minimizes the secrecy outage probability of the system is obtained, illustrating its dependence on the eavesdropper's location. We then derive an easy-to-compute expression for the secrecy outage probability when our proposed LBB scheme is adopted. We also consider the positive impact a friendly jammer can have on our beamforming solution, showing how the path to optimality remains the same. Finally, we investigate the impact of location uncertainty on the secrecy outage probability, showing how our solution can still allow for secrecy even when the source only has a noisy estimate of the eavesdropper's location. Our work demonstrates how a multi-antenna array, operating in the most general channel conditions and most likely system set-up, can be configured rapidly in the field so as to deliver an optimal physical layer security solution.Comment: 11 pages, 8 figures. Accepted for publication in IEEE Transactions on Wireless Communications. arXiv admin note: substantial text overlap with arXiv:1510.0856

Energy Efficiency in MIMO Underlay and Overlay Device-to-Device Communications and Cognitive Radio Systems

This paper addresses the problem of resource allocation for systems in which a primary and a secondary link share the available spectrum by an underlay or overlay approach. After observing that such a scenario models both cognitive radio and D2D communications, we formulate the problem as the maximization of the secondary energy efficiency subject to a minimum rate requirement for the primary user. This leads to challenging non-convex, fractional problems. In the underlay scenario, we obtain the global solution by means of a suitable reformulation. In the overlay scenario, two algorithms are proposed. The first one yields a resource allocation fulfilling the first-order optimality conditions of the resource allocation problem, by solving a sequence of easier fractional problems. The second one enjoys a weaker optimality claim, but an even lower computational complexity. Numerical results demonstrate the merits of the proposed algorithms both in terms of energy-efficient performance and complexity, also showing that the two proposed algorithms for the overlay scenario perform very similarly, despite the different complexity.Comment: to appear in IEEE Transactions on Signal Processin