Resource allocation for 5G technologies under statistical queueing constraints

As the launch of fifth generation (5G) wireless networks is approaching, recent years have witnessed comprehensive discussions about a possible 5G standard. Many transmission scenarios and technologies have been proposed and initial over-the-air experimental trials have been conducted. Most of the existing literature studies on 5G technologies have mainly focused on the physical layer parameters and quality of service (QoS) requirements, e.g., achievable data rates. However, the demand for delay-sensitive data traffic over wireless networks has increased exponentially in the recent years, and is expected to further increase by the time of 5G. Therefore, other constraints at the data-link layer concerning the buffer overflow and delay violation probabilities should also be regarded. It follows that evaluating the performance of the 5G technologies when such constraints are considered is a timely task. Motivated by this fact, in this thesis we explore the performance of three promising 5G technologies when operating under certain QoS at the data-link layer. We follow a cross-layer approach to examine the interplay between the physical and data-link layers when statistical QoS constraints are inflicted in the form of limits on the delay violation and buffer overflow probabilities. Noting that wireless systems, generally, have limited physical resources, in this thesis we mainly target designing adaptive resource allocation schemes to maximize the system performance under such QoS constraints. We initially investigate the throughput and energy efficiency of a general class of multiple-input multiple-output (MIMO) systems with arbitrary inputs. As a cross-layer evaluation tool, we employ the effective capacity as the main performance metric, which is the maximum constant data arrival rate at a buffer that can be sustained by the channel service process under specified QoS constraints. We obtain the optimal input covariance matrix that maximizes the effective capacity under a short-term average power budget. Then, we perform an asymptotic analysis of the effective capacity in the low signal-to-noise ratio and large-scale antenna (massive MIMO) regimes. Such analysis has a practical importance for 5G scenarios that necessitate low latency, low power consumption, and/or ability to simultaneously support massive number of users. Non-orthogonal multiple access (NOMA) has attracted significant attention in the recent years as a promising multiple access technology for 5G. In this thesis, we consider a two-user power-domain NOMA scheme in which both transmitters employ superposition coding and the receiver applies successive interference cancellation (SIC) with a certain order. For practical concerns, we consider limited transmission power budgets at the transmitters, and assume that both transmitters have arbitrarily distributed input signals. We again exploit the effective capacity as the main cross-layer performance measure. We provide a resource management scheme that can jointly obtain the optimal power allocation policies at the transmitters and the optimal decoding order at the receiver, with the goal of maximizing the effective capacity region that provides the maximum allowable sustainable arrival rate region at the transmitters' buffers under QoS guarantees. In the recent years, visible light communication (VLC) has emerged as a potential transmission technology that can utilize the visible light spectrum for data transmission along with illumination. Different from the existing literature studies on VLC, in this thesis we consider a VLC system in which the access point (AP) is unaware of the channel conditions, thus the AP sends the data at a fixed rate. Under this assumption, and considering an ON-OFF data source, we provide a cross-layer study when the system is subject to statistical buffering constraints. To this end, we employ the maximum average data arrival rate at the AP buffer and the non-asymptotic bounds on buffering delay as the main performance measures. To facilitate our analysis, we adopt a two-state Markov process to model the fixed-rate transmission strategy, and we then formulate the steady-state probabilities of the channel being in the ON and OFF states. The coexistence of radio frequency (RF) and VLC systems in typical indoor environments can be leveraged to support vast user QoS needs. In this thesis, we examine the benefits of employing both technologies when operating under statistical buffering limitations. Particularly, we consider a multi-mechanism scenario that utilizes RF and VLC links for data transmission in an indoor environment. As the transmission technology is the main physical resource to be concerned in this part, we propose a link selection process through which the transmitter sends data over the link that sustains the desired QoS guarantees the most. Considering an ON-OFF data source, we employ the maximum average data arrival rate at the transmitter buffer and the non-asymptotic bounds on data buffering delay as the main performance measures. We formulate the performance measures under the assumption that both links are subject to average and peak power constraints

Resource allocation under delay-guarantee constraints for visible-light communication

Visible Light Communications (VLC) relying on commercially available Light-Emitting Diode (LED) transmitters offer a huge data rate potential in this license-free spectral domain, whilst simultaneously satisfying energy-efficient conventional illumination demands. In a LED based VLC system, the achievable data rate may be severely degraded owing to the interference, when the VLC system employs the Unity Frequency Reuse (UFR) approach. In order to mitigate the effects of interference, we propose a pair of interference avoidance approaches, namely Frequency Reuse (FR) based transmission and Vectored Transmission (VT). Furthermore, the Resource Allocation (RA) problems of indoor Mobile Terminals (MTs) are investigated based on different transmission strategies. Inspired by the concept of Effective Capacity (EC), we formulate our RA optimization problems applying proportional fairness, while satisfying specific statistical delay constraints. Our optimization procedure solves the resource allocation problem of indoor MTs with the aid of a decentralized algorithm. Simulation results are also presented for quantifying the performance of the proposed algorithm. It is shown that both of our interference avoidance approaches are capable of reducing the interference, hence improving the overall performance. Furthermore, it is also observed that our VT transmission is capable of achieving a higher effective capacity than the FR approach, when the statistical delay requirements are loose. By contrast, the FR based transmission attains the best performance, when we tighten the delay requirements

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

Load balancing in hybrid LiFi and RF networks

The increasing number of mobile devices challenges the current radio frequency (RF) networks. The conventional RF spectrum for wireless communications is saturating, motivating to develop other unexplored frequency bands. Light Fidelity (LiFi) which uses more than 300 THz of the visible light spectrum for high-speed wireless communications, is considered a promising complementary technology to its RF counterpart. LiFi enables daily lighting infrastructures, i.e. light emitting diode (LED) lamps to realise data transmission, and maintains the lighting functionality at the same time. Since LiFi mainly relies on line-of-sight (LoS) transmission, users in indoor environments may experience blockages which significantly affects users’ quality of service (QoS). Therefore, hybrid LiFi and RF networks (HLRNs) where LiFi supports high data rate transmission and RF offers reliable connectivity, can provide a potential solution to future indoor wireless communications. In HLRNs, efficient load balancing (LB) schemes are critical in improving the traffic performance and network utilisation. In this thesis, the optimisation-based scheme (OBS) and the evolutionary game theory (EGT) based scheme (EGTBS) are proposed for load balancing in HLRNs. Specifically, in OBS, two algorithms, the joint optimisation algorithm (JOA) and the separate optimisation algorithm (SOA) are proposed. Analysis and simulation results show that JOA can achieve the optimal performance in terms of user data rate while requiring high computational complexity. SOA reduces the computational complexity but achieves low user data rates. EGTBS is able to achieve a better performance/complexity trade-off than OBS and other conventional load balancing schemes. In addition, the effects of handover, blockages, orientation of LiFi receivers, and user data rate requirement on the throughput of HLRNs are investigated. Moreover, the packet latency in HLRNs is also studied in this thesis. The notion of LiFi service ratio is introduced, defined as the proportion of users served by LiFi in HLRNs. The optimal LiFi service ratio to minimise system delay is mathematically derived and a low-complexity packet flow assignment scheme based on this optimum ratio is proposed. Simulation results show that the theoretical optimum of the LiFi service ratio is very close to the practical solution. Also, the proposed packet flow assignment scheme can reduce at most 90% of packet delay compared to the conventional load balancing schemes at reduced computational complexity