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

Scheduling algorithms for next generation cellular networks

Next generation wireless and mobile communication systems are rapidly evolving to satisfy the demands of users. Due to spectrum scarcity and time-varying nature of wireless networks, supporting user demand and achieving high performance necessitate the design of efficient scheduling and resource allocation algorithms. Opportunistic scheduling is a key mechanism for such a design, which exploits the time-varying nature of the wireless environment for improving the performance of wireless systems. In this thesis, our aim is to investigate various categories of practical scheduling problems and to design efficient policies with provably optimal or near-optimal performance. An advantage of opportunistic scheduling is that it can effectively be incorporated with new communication technologies to further increase the network performance. We investigate two key technologies in this context. First, motivated by the current under-utilization of wireless spectrum, we characterize optimal scheduling policies for wireless cognitive radio networks by assuming that users always have data to transmit. We consider cooperative schemes in which secondary users share the time slot with primary users in return for cooperation, and our aim is to improve the primary systems performance over the non-cooperative case. By employing Lyapunov Optimization technique, we develop optimal scheduling algorithms which maximize the total expected utility and satisfy the minimum data rate requirements of the primary users. Next, we study scheduling problem with multi-packet transmission. The motivation behind multi-packet transmission comes from the fact that the base station can send more than one packets simultaneously to more than one users. By considering unsaturated queueing systems we aim to stabilize user queues. To this end, we develop a dynamic control algorithm which is able to schedule more than one users in a time slot by employing hierarchical modulation which enables multi-packet transmission. Through Lyapunov Optimization technique, we show that our algorithm is throughput-optimal. We also study the resulting rate region of developed policy and show that it is larger than that of single user scheduling. Despite the advantage of opportunistic scheduling, this mechanism requires that the base station is aware of network conditions such as channel state and queue length information of users. In the second part of this thesis, we turn our attention to the design of scheduling algorithms when complete network information is not available at the scheduler. In this regard, we study three sets of problems where the common objective is to stabilize user queues. Specifically, we first study a cellular downlink network by assuming that channels are identically distributed across time slots and acquiring channel state information of a user consumes a certain fraction of resource which is otherwise used for transmission of data. We develop a joint scheduling and channel probing algorithm which collects channel state information from only those users with su±ciently good channel quality. We also quantify the minimum number of users that must exist to achieve larger rate region than Max-Weight algorithm with complete channel state information. Next, we consider a more practical channel models where channels can be time-correlated (possibly non-stationary) and only a fixed number of channels can be probed. We develop learning based scheduling algorithm which tracks and predicts instantaneous transmission rates of users and makes a joint scheduling and probing decision based on the predicted rates rather than their exact values. We also characterize the achievable rate region of these policies as compared to Max-Weight policy with exact channel state information. Finally, we study a cellular uplink system and develop a fully distributed scheduling algorithm which can perform over general fading channels and does not require explicit control messages passing among the users. When continuous backoff time is allowed, we show that the proposed distributed algorithm can achieve the same performance as that of centralized Max-Weight algorithm in terms of both throughput and delay. When backoff time can take only discrete values, we show that our algorithm can perform well at the expense of low number of mini-slots for collision resolution

Power Allocation Schemes for Multicell Massive MIMO Systems

This paper investigates the sum-rate gains brought by power allocation strategies in multicell massive multipleinput multiple-output systems, assuming time-division duplex transmission. For both uplink and downlink, we derive tractable expressions for the achievable rate with zero-forcing receivers and precoders respectively. To avoid high complexity joint optimization across the network, we propose a scheduling mechanism for power allocation, where in a single time slot, only cells that do not interfere with each other adjust their transmit powers. Based on this, corresponding transmit power allocation strategies are derived, aimed at maximizing the sum rate per-cell. These schemes are shown to bring considerable gains over equal power allocation for practical antenna configurations (e.g., up to a few hundred). However, with fixed number of users (N), these gains diminish as M turns to infinity, and equal power allocation becomes optimal. A different conclusion is drawn for the case where both M and N grow large together, in which case: (i) improved rates are achieved as M grows with fixed M/N ratio, and (ii) the relative gains over the equal power allocation diminish as M/N grows. Moreover, we also provide applicable values of M/N under an acceptable power allocation gain threshold, which can be used as to determine when the proposed power allocation schemes yield appreciable gains, and when they do not. From the network point of view, the proposed scheduling approach can achieve almost the same performance as the joint power allocation after one scheduling round, with much reduced complexity

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Multi-Antenna Techniques for Next Generation Cellular Communications

Future cellular communications are expected to offer substantial improvements for the pre- existing mobile services with higher data rates and lower latency as well as pioneer new types of applications that must comply with strict demands from a wider range of user types. All of these tasks require utmost efficiency in the use of spectral resources. Deploying multiple antennas introduces an additional signal dimension to wireless data transmissions, which provides a significant alternative solution against the plateauing capacity issue of the limited available spectrum. Multi-antenna techniques and the associated key enabling technologies possess unquestionable potential to play a key role in the evolution of next generation cellular systems. Spectral efficiency can be improved on downlink by concurrently serving multiple users with high-rate data connections on shared resources. In this thesis optimized multi-user multi-input multi-output (MIMO) transmissions are investigated on downlink from both filter design and resource allocation/assignment points of view. Regarding filter design, a joint baseband processing method is proposed specifically for high signal-to-noise ratio (SNR) conditions, where the necessary signaling overhead can be compensated for. Regarding resource scheduling, greedy- and genetic-based algorithms are proposed that demand lower complexity with large number of resource blocks relative to prior implementations. Channel estimation techniques are investigated for massive MIMO technology. In case of channel reciprocity, this thesis proposes an overhead reduction scheme for the signaling of user channel state information (CSI) feedback during a relative antenna calibration. In addition, a multi-cell coordination method is proposed for subspace-based blind estimators on uplink, which can be implicitly translated to downlink CSI in the presence of ideal reciprocity. Regarding non-reciprocal channels, a novel estimation technique is proposed based on reconstructing full downlink CSI from a select number of dominant propagation paths. The proposed method offers drastic compressions in user feedback reports and requires much simpler downlink training processes. Full-duplex technology can provide up to twice the spectral efficiency of conventional resource divisions. This thesis considers a full-duplex two-hop link with a MIMO relay and investigates mitigation techniques against the inherent loop-interference. Spatial-domain suppression schemes are developed for the optimization of full-duplex MIMO relaying in a coverage extension scenario on downlink. The proposed methods are demonstrated to generate data rates that closely approximate their global bounds