Multiuser beam steering OWC system based on NOMA

In this paper, we propose applying Non-Orthogonal Multiple Access (NOMA) technology in a multiuser beam steering OWC system. We study the performance of the NOMA-based multiuser beam steering system in terms of the achievable rate and Bit Error Rate (BER). We investigate the impact of the power allocation factor of NOMA and the number of users in the room. The results show that the power allocation factor is a vital parameter in NOMA-based transmission that affects the performance of the network in terms of data rate and BER.Comment: ICTON 202

Cross-layer optimization of unequal protected layered video over hierarchical modulation

Abstract-unequal protection mechanisms have been proposed at several layers in order to improve the reliability of multimedia contents, especially for video data. The paper aims at implementing a multi-layer unequal protection scheme, which is based on a Physical-Transport-Application cross-layer design. Hierarchical modulation, in the physical layer, has been demonstrated to increase the overall user capacity of a wireless communications. On the other hand, unequal erasure protection codes at the transport layer turned out to be an efficient method to protect video data generated by the application layer by exploiting their intrinsic properties. In this paper, the two techniques are jointly optimized in order to enable recovering lost data in case the protection is performed separately. We show that the cross-layer design proposed herein outperforms the performance of hierarchical modulation and unequal erasure codes taken independently

Lightly synchronized Multipacket Reception in Machine-Type Communications Networks

Machine Type Communication (MTC) applications were designed to monitor and control elements of our surroundings and environment. MTC applications have a different set of requirements compared to the traditional communication devices, with Machine to Machine (M2M) data being mostly short, asynchronous, bursty and sometimes requiring end-to-end delays below 1ms. With the growth of MTC, the new generation of mobile communications has to be able to present different types of services with very different requirements, i.e. the same network has to be capable of "supplying" connection to the user that just wants to download a video or use social media, allowing at the same time MTC that has completely different requirements, without deteriorating both experiences. The challenges associated to the implementation of MTC require disruptive changes at the Physical (PHY) and Medium Access Control (MAC) layers, that lead to a better use of the spectrum available. The orthogonality and synchronization requirements of the PHY layer of current Long Term Evolution Advanced (LTE-A) radio access network (based on glsofdm and Single Carrier Frequency Domain Equalization (SC-FDE)) are obstacles for this new 5th Generation (5G) architecture. Generalized Frequency Division Multiplexing (GFDM) and other modulation techniques were proposed as candidates for the 5G PHY layer, however they also suffer from visible degradation when the transmitter and receiver are not synchronized, leading to a poor performance when collisions occur in an asynchronous MAC layer. This dissertation addresses the requirements of M2M traffic at the MAC layer applying multipacket reception (MPR) techniques to handle the bursty nature of the traffic and synchronization tones and optimized back-off approaches to reduce the delay. It proposes a new MAC protocol and analyses its performance analytically considering an SC-FDE modulation. The models are validated using a system level cross-layer simulator developed in MATLAB, which implements the MAC protocol and applies PHY layer performance models. The results show that the MAC’s latency depends mainly on the number of users and the load of each user, and can be controlled using these two parameters

Multiuser Diversity Management for Multicast/Broadcast Services in 5G and Beyond Networks

The envisaged fifth-generation (5G) and beyond networks represent a paradigm shift for global communications, offering unprecedented breakthroughs in media service delivery with novel capabilities and use cases. Addressing the critical research verticals and challenges that characterize the International Mobile Telecommunications (IMT)-2030 framework requires a compelling mix of enabling radio access technologies (RAT) and native softwarized, disaggregated, and intelligent radio access network (RAN) conceptions. In such a context, the multicast/broadcast ser vice (MBS) capability is an appealing feature to address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. Embracing the MBS capability as a primary component of the envisaged 5G and beyond networks comes with multiple open challenges. In this research, we contextualize and address the necessity of ensuring stringent quality of service (QoS)/quality of experience (QoE) requirements, multicasting over millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies, and handling complex mobility behaviors. In the broad problem space around these three significant challenges, we focus on the specific research problems of effectively handling the trade-off between multicasting gain and multiuser diversity, along with the trade-off between optimal network performance and computational complexity. In this research, we cover essential aspects at the intersection of MBS, radio resource management (RRM), machine learning (ML), and the Open RAN (O-RAN) framework. We characterize and address the dynamic multicast multiuser diversity through low-complexity RRM solutions aided by ML, orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques in 5G MBS and beyond networks. We characterize the performance of the multicast access techniques conventional multicast scheme (CMS), subgrouping based on OMA (S-OMA), and subgrouping based on NOMA (S-NOMA). We provide conditions for their adequate selection regarding the specific network conditions (Chapter 4). Consequently, we propose heuristic methods for the dynamic multicast access technique selection and resource allocation, taking advantage of the multiuser diversity (Chapter 5.1). Moreover, we proposed a multicasting strategy based on fixed pre-computed multiple-input multiple-output (MIMO) multi-beams and S-NOMA (Chapter 5.2). Our approach tackles specific throughput requirements for enabling extended reality (XR) applications attending multiple users and handling their spatial and channel quality diversity. We address the computational complexity (CC) associated with the dynamic multicast RRM strategies and highlight the implications of fast variations in the reception conditions of the multicast group (MG) members. We propose a low complexity ML-based solution structured by a multicast-oriented trigger to avoid overrunning the algorithm, a K-Means clustering for group-oriented detection and splitting, and a classifier for selecting the most suitable multicast access technique (Chapter 6.1). Our proposed approaches allow addressing the trade-off between optimal network performance and CC by maximizing specific QoS parameters through non-optimal solutions, considerably reducing the CC of conventional exhaustive mechanisms. Moreover, we discuss the insertion of ML-based multicasting RRM solutions into the envisioned disaggregated O-RAN framework (Chapter 6.2.5). We analyze specific MBS tasks and the importance of a native decentralized, softwarized, and intelligent conception. We assess the effectiveness of our proposal under multiple numerical and link level simulations of recreated 5G MBS use cases operating in μWave and mmWave. We evaluate various network conditions, service constraints, and users’ mobility behaviors