Efficient radio resource management for future generation heterogeneous wireless networks

The heterogeneous deployment of small cells (e.g., femtocells) in the coverage area of the traditional macrocells is a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the future fifth generation (5G) wireless networks. However, the unplanned and ultra-dense deployment of femtocells with their uncoordinated operations will result in technical challenges such as severe interference, a significant increase in total energy consumption, unfairness in radio resource sharing and inadequate quality of service provisioning. Therefore, there is a need to develop efficient radio resource management algorithms that will address the above-mentioned technical challenges. The aim of this thesis is to develop and evaluate new efficient radio resource management algorithms that will be implemented in cognitive radio enabled femtocells to guarantee the economical sustainability of broadband wireless communications and users' quality of service in terms of throughput and fairness. Cognitive Radio (CR) technology with the Dynamic Spectrum Access (DSA) and stochastic process are the key technologies utilized in this research to increase the spectrum efficiency and energy efficiency at limited interference. This thesis essentially investigates three research issues relating to the efficient radio resource management: Firstly, a self-organizing radio resource management algorithm for radio resource allocation and interference management is proposed. The algorithm considers the effect of imperfect spectrum sensing in detecting the available transmission opportunities to maximize the throughput of femtocell users while keeping interference below pre-determined thresholds and ensuring fairness in radio resource sharing among users. Secondly, the effect of maximizing the energy efficiency and the spectrum efficiency individually on radio resource management is investigated. Then, an energy-efficient radio resource management algorithm and a spectrum-efficient radio resource management algorithm are proposed for green communication, to improve the probabilities of spectrum access and further increase the network capacity for sustainable environments. Also, a joint maximization of the energy efficiency and spectrum efficiency of the overall networks is considered since joint optimization of energy efficiency and spectrum efficiency is one of the goals of 5G wireless networks. Unfortunately, maximizing the energy efficiency results in low performance of the spectrum efficiency and vice versa. Therefore, there is an investigation on how to balance the trade-off that arises when maximizing both the energy efficiency and the spectrum efficiency simultaneously. Hence, a joint energy efficiency and spectrum efficiency trade-off algorithm is proposed for radio resource allocation in ultra-dense heterogeneous networks based on orthogonal frequency division multiple access. Lastly, a joint radio resource allocation with adaptive modulation and coding scheme is proposed to minimize the total transmit power across femtocells by considering the location and the service requirements of each user in the network. The performance of the proposed algorithms is evaluated by simulation and numerical analysis to demonstrate the impact of ultra-dense deployment of femtocells on the macrocell networks. The results show that the proposed algorithms offer improved performance in terms of throughput, fairness, power control, spectrum efficiency and energy efficiency. Also, the proposed algorithms display excellent performance in dynamic wireless environments

User Association in 5G Networks: A Survey and an Outlook

26 pages; accepted to appear in IEEE Communications Surveys and Tutorial

5G Backhaul Challenges and Emerging Research Directions: A Survey

5G is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the Internet of Things. Focused research and standardization work have been addressing the corresponding challenges from the radio perspective while employing advanced features, such as network densi cation, massive multiple-input-multiple-output antennae, coordinated multi-point processing, intercell interference mitigation techniques, carrier aggregation, and new spectrum exploration. Nevertheless, a new bottleneck has emerged: the backhaul. The ultra-dense and heavy traf c cells should be connected to the core network through the backhaul, often with extreme requirements in terms of capacity, latency, availability, energy, and cost ef ciency. This pioneering survey explains the 5G backhaul paradigm, presents a critical analysis of legacy, cutting-edge solutions, and new trends in backhauling, and proposes a novel consolidated 5G backhaul framework. A new joint radio access and backhaul perspective is proposed for the evaluation of backhaul technologies which reinforces the belief that no single solution can solve the holistic 5G backhaul problem. This paper also reveals hidden advantages and shortcomings of backhaul solutions, which are not evident when backhaul technologies are inspected as an independent part of the 5G network. This survey is key in identifying essential catalysts that are believed to jointly pave the way to solving the beyond-2020 backhauling challenge. Lessons learned, unsolved challenges, and a new consolidated 5G backhaul vision are thus presented

Random Linear Network Coding for 5G Mobile Video Delivery

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA