A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution towards 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, etc. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

L'abstract è presente nell'allegato / the abstract is in the attachmen

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

Hybrid generalized non-orthogonal multiple access for the 5G wireless networks.

Master of Science in Computer Engineering. University of KwaZulu-Natal. Durban, 2018.The deployment of 5G networks will lead to an increase in capacity, spectral efficiency, low latency and massive connectivity for wireless networks. They will still face the challenges of resource and power optimization, increasing spectrum efficiency and energy optimization, among others. Furthermore, the standardized technologies to mitigate against the challenges need to be developed and are a challenge themselves. In the current predecessor LTE-A networks, orthogonal frequency multiple access (OFDMA) scheme is used as the baseline multiple access scheme. It allows users to be served orthogonally in either time or frequency to alleviate narrowband interference and impulse noise. Further spectrum limitations of orthogonal multiple access (OMA) schemes have resulted in the development of non-orthogonal multiple access (NOMA) schemes to enable 5G networks to achieve high spectral efficiency and high data rates. NOMA schemes unorthogonally co-multiplex different users on the same resource elements (RE) (i.e. time-frequency domain, OFDMA subcarrier, or spreading code) via power domain (PD) or code domain (CD) at the transmitter and successfully separating them at the receiver by applying multi-user detection (MUD) algorithms. The current developed NOMA schemes, refered to as generalized-NOMA (G-NOMA) technologies includes; Interleaver Division Multiple Access (IDMA, Sparse code multiple access (SCMA), Low-density spreading multiple access (LDSMA), Multi-user shared access (MUSA) scheme and the Pattern Division Multiple Access (PDMA). These protocols are currently still under refinement, their performance and applicability has not been thoroughly investigated. The first part of this work undertakes a thorough investigation and analysis of the performance of the existing G-NOMA schemes and their applicability. Generally, G-NOMA schemes perceives overloading by non-orthogonal spectrum resource allocation, which enables massive connectivity of users and devices, and offers improved system spectral efficiency. Like any other technologies, the G-NOMA schemes need to be improved to further harvest their benefits on 5G networks leading to the requirement of Hybrid G-NOMA (G-NOMA) schemes. The second part of this work develops a HG-NOMA scheme to alleviate the 5G challenges of resource allocation, inter and cross-tier interference management and energy efficiency. This work develops and investigates the performance of an Energy Efficient HG-NOMA resource allocation scheme for a two-tier heterogeneous network that alleviates the cross-tier interference and improves the system throughput via spectrum resource optimization. By considering the combinatorial problem of resource pattern assignment and power allocation, the HG-NOMA scheme will enable a new transmission policy that allows more than two macro-user equipment’s (MUEs) and femto-user equipment’s (FUEs) to be co-multiplexed on the same time-frequency RE increasing the spectral efficiency. The performance of the developed model is shown to be superior to the PD-NOMA and OFDMA schemes