Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

State-of-the-art assessment of 5G mmWave communications

Deliverable D2.1 del proyecto 5GWirelessMain objective of the European 5Gwireless project, which is part of the H2020 Marie Slodowska- Curie ITN (Innovative Training Networks) program resides in the training and involvement of young researchers in the elaboration of future mobile communication networks, focusing on innovative wireless technologies, heterogeneous network architectures, new topologies (including ultra-dense deployments), and appropriate tools. The present Document D2.1 is the first deliverable of Work- Package 2 (WP2) that is specifically devoted to the modeling of the millimeter-wave (mmWave) propagation channels, and development of appropriate mmWave beamforming and signal processing techniques. Deliver D2.1 gives a state-of-the-art on the mmWave channel measurement, characterization and modeling; existing antenna array technologies, channel estimation and precoding algorithms; proposed deployment and networking techniques; some performance studies; as well as a review on the evaluation and analysis toolsPostprint (published version

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications

Multi-user Downlink Beamforming using Uplink Downlink Duality with CEQs for Frequency Selective Channels

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant envelope quantized (CEQ) digital-to-analog converters on each radio frequency chain communicates with multiple single antenna users with individual signal-to-quantization-plus-interference-plus-noise ratio (SQINR) constraints over frequency selective channels. We first establish uplink downlink duality for the system with CEQ hardware constraints and OFDM-based transmission considered in this paper. Based on the uplink downlink duality principle, we present a solution to the multi-user multi-carrier beamforming and power allocation problem that maximizes the minimum SQINR over all users and sub-carriers. We then present a per sub-carrier version of the originally proposed solution that decouples all sub-carriers of the OFDM waveform resulting in smaller sub-problems that can be solved in a parallel manner. Our numerical results based on 3GPP channel models generated from Quadriga demonstrate improvements in terms of ergodic sum rate and ergodic minimum rate over state-of-the-art linear solutions. We also show improved performance over non-linear solutions in terms of the coded bit error rate with the increased flexibility of assigning individual user SQINRs built into the proposed framework.Comment: arXiv admin note: text overlap with arXiv:2206.1442

Four-element phased-array beamformers and a self-interference canceling full-duplex transciver in 130-nm SiGe for 5G applications at 26 GHz

This thesis is on the design of radio-frequency (RF) integrated front-end circuits for next generation 5G communication systems. The demand for higher data rates and lower latency in 5G networks can only be met using several new technologies including, but not limited to, mm-waves, massive-MIMO, and full-duplex. Use of mm-waves provides more bandwidth that is necessary for high data rates at the cost of increased attenuation in air. Massive-MIMO arrays are required to compensate for this increased path loss by providing beam steering and array gain. Furthermore, full duplex operation is desirable for improved spectrum efficiency and reduced latency. The difficulty of full duplex operation is the self-interference (SI) between transmit (TX) and receive (RX) paths. Conventional methods to suppress this interference utilize either bulky circulators, isolators, couplers or two separate antennas. These methods are not suitable for fully-integrated full-duplex massive-MIMO arrays. This thesis presents circuit and system level solutions to the issues summarized above, in the form of SiGe integrated circuits for 5G applications at 26 GHz. First, a full-duplex RF front-end architecture is proposed that is scalable to massive-MIMO arrays. It is based on blind, RF self-interference cancellation that is applicable to single/shared antenna front-ends. A high resolution RF vector modulator is developed, which is the key building block that empowers the full-duplex frontend architecture by achieving better than state-of-the-art 10-b monotonic phase control. This vector modulator is combined with linear-in-dB variable gain amplifiers and attenuators to realize a precision self-interference cancellation circuitry. Further, adaptive control of this SI canceler is made possible by including an on-chip low-power IQ downconverter. It correlates copies of transmitted and received signals and provides baseband/dc outputs that can be used to adaptively control the SI canceler. The solution comes at the cost of minimal additional circuitry, yet significantly eases linearity requirements of critical receiver blocks at RF/IF such as mixers and ADCs. Second, to complement the proposed full-duplex front-end architecture and to provide a more complete solution, high-performance beamformer ICs with 5-/6- b phase and 3-/4-b amplitude control capabilities are designed. Single-channel, separate transmitter and receiver beamformers are implemented targeting massive- MIMO mode of operation, and their four-channel versions are developed for phasedarray communication systems. Better than state-of-the-art noise performance is obtained in the RX beamformer channel, with a full-channel noise figure of 3.3 d