Performance analysis of biological resource allocation algorithms for next generation networks.

Masters Degree. University of KwaZulu-Natal, Durban.Abstract available in PDF.Publications listed on page iii

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

Nonorthogonal Multiple Access for 5G and Beyond

This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N029720/1 and Grant EP/N029720/2. The work of L. Hanzo was supported by the ERC Advanced Fellow Grant Beam-me-up

Investigation on Evolving Single-Carrier NOMA into Multi-Carrier NOMA in 5G

© 2013 IEEE. Non-orthogonal multiple access (NOMA) is one promising technology, which provides high system capacity, low latency, and massive connectivity, to address several challenges in the fifth-generation wireless systems. In this paper, we first reveal that the NOMA techniques have evolved from single-carrier NOMA (SC-NOMA) into multi-carrier NOMA (MC-NOMA). Then, we comprehensively investigated on the basic principles, enabling schemes and evaluations of the two most promising MC-NOMA techniques, namely sparse code multiple access (SCMA) and pattern division multiple access (PDMA). Meanwhile, we consider that the research challenges of SCMA and PDMA might be addressed with the stimulation of the advanced and matured progress in SC-NOMA. Finally, yet importantly, we investigate the emerging applications, and point out the future research trends of the MC-NOMA techniques, which could be straightforwardly inspired by the various deployments of SC-NOMA

Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

Spectrally efficient Non-Orthogonal Multiple Access (NOMA) techniques for future generation mobile systems

With the expectation of over a 1000-fold increase in the number of connected devices by 2020, efficient utilization of the limited bandwidth has become ever more important in the design of mobile wireless systems. Furthermore, the ever-increasing demand for higher data rates has made it necessary for a new waveform design that satisfies not only throughput demands, but network capacity as well. One such technique recently proposed is the non-orthogonal multiple access (NOMA) which utilizes the distance-dependent power domain multiplexing, based on the principles of signal superposition. In this thesis, new spectrally efficient non-orthogonal signal techniques are proposed. The goal of the schemes is to allow simultaneous utilization of the same time frequency network resources. This is achieved by designing component signals in both power and phase domain such that users are precoded or preformed to form a single and uniquely decodable composite signal. The design criteria are based on maximizing either the sum rate or spectral efficiency, minimizing multi-user interference and detection ambiguity, and maximizing the minimum Euclidean distance between the composite constellation points. The design principles are applied in uplink, downlink and coordinated multipoint (CoMP) scenarios. We assume ideal channel state with perfect estimation, low mobility and synchronization scenarios so as to prove the concept and serve as a bound for any future work in non-ideal conditions. Extensive simulations and numerical analysis are carried to show the superiority and compatibility of the schemes. First, a new NOMA signal design called uplink NOMA with constellation precoding is proposed. The precoding weights are generated at the eNB based on the number of users to be superposed. The eNB signals the precoding weights to be employed by the users to adjust their transmission. The adjustments utilize the channel state information estimated from common periodic pilots broadcasted by the eNB. The weights ensure the composite received signal at the eNB belongs to the pre-known constellation. Furthermore, the users precode to the eNB antenna that requires the least total transmit power from all the users. At the eNB, joint maximum likelihood (JML) detection is employed to recover the component signals. As the composite constellation is as that of a single user transmitting that same constellation, multiple access interference can be viewed as absent, which allows multiple users to transmit at their full rates. Furthermore, the power gain achieved by the sum of the component signals maximizes the sum rate. Secondly, the constellation design principle is employed in the downlink scenario. In the scheme, called downlink NOMA with constellation preforming, the eNB preforms the users signal with power and phase weights prior to transmission. The preforming ensures multi-user interference is eliminated and the spectral efficiency maximized. The preformed composite constellation is broadcasted by the eNB which is received by all users. Subsequently, the users perform JML detection with the designed constellation to extract their individual component signals. Furthermore, improved signal reliability is achieved in transmit and receive diversity scenarios in the schemes called distributed transmit and receive diversity combining, respectively. Thirdly, the constellation preforming on the downlink is extended to MIMO spatial multiplexing scenarios. The first MIMO scheme, called downlink NOMA with constellation preforming, each eNB antenna transmits a preformed composite signal composed of a set of multiple users’ streams. This achieves spatial multiplexing with diversity with less transmit antennas, reducing costs associated with multiple RF chains, while still maximizing the sum rate. In the second MIMO scheme, a highly spectrally efficient MIMO preforming scheme is proposed. The scheme, called group layer MIMO with constellation preforming, the eNB preforms to a specific group of users on each transmit antenna. In all the schemes, the users perform JML detection to recover their signals. Finally, the adaptability of the constellation design is shown in CoMP. The scheme, called CoMP with joint constellation processing, the additional degrees of freedom, in form of interfering eNBs, are utilized to enable spatial multiplexing to a user with a single receive antenna. This is achieved by precoding each stream from the coordinating eNB with weights signalled by a central eNB. Consequently, the inter-cell interference is eliminated and the sum-rate maximized. To reduce the total power spent on precoding, an active cell selection scheme is proposed where the precoding is employed on the highest interferers to the user. Furthermore, a power control scheme is applied the design principle, where the objective is to reduce cross-layer interference by adapting the transmission power to the mean channel gain