Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate on the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmissions are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.Comment: 34 pages, 10 figures, a survey paper accepted by the IEEE JSAC special issue on Next Generation Multiple Acces

Massive MIMO systems for 5G: a systematic mapping study on antenna design challenges and channel estimation open issues

The next generation of mobile networks (5G) is expected to achieve high data rates, reduce latency, as well as improve the spectral and energy efficiency of wireless communication systems. Several technologies are being explored to be used in 5G systems. One of the main promising technologies that is seen to be the enabler of 5G is massive multiple-input multiple-output (mMIMO) systems. Numerous studies have indicated the utility of mMIMO in upcoming wireless networks. However, there are several challenges that needs to be unravelled. In this paper, the latest progress of research on challenges in mMIMO systems is tracked, in the context of mutual coupling, antenna selection, pilot contamination and feedback overhead. The results of a systematic mapping study performed on 63 selected primary studies, published between the year 2017 till the second quarter of 2020, are presented. The main objective of this secondary study is to identify the challenges regarding antenna design and channel estimation, give an overview on the state-of-the-art solutions proposed in the literature, and finally, discuss emerging open research issues that need to be considered before the implementation of mMIMO systems in 5G networks

Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa

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

Near far resistant detection for CDMA personal communication systems.

The growth of Personal Communications, the keyword of the 90s, has already the signs of a technological revolution. The foundations of this revolution are currently set through the standardization of the Universal Mobile Telecommunication System (UMTS), a communication system with synergistic terrestrial and satellite segments. The main characteristic of the UMTS radio interface, is the provision of ISDN services. Services with higher than voice data rates require more spectrum, thus techniques that utilize spectrum as efficiently as possible are currently at the forefront of the research community interests. Two of the most spectrally efficient multiple access technologies, namely. Code Division Multiple Access (CDMA) and Time Division Multiple Access (TDMA) concentrate the efforts of the European telecommunity.This thesis addresses problems and. proposes solutions for CDMA systems that must comply with the UMTS requirements. Prompted by Viterbi's call for further extending the potential of CDMA through signal processing at the receiving end, we propose new Minimum Mean Square Error receiver architectures. MMSE detection schemes offer significant advantages compared to the conventional correlation based receivers as they are NEar FAr Resistant (NEFAR) over a wide range of interfering power levels. The NEFAR characteristic of these detectors reduces considerably the requirements of the power control loops currently found in commercial CDMA systems. MMSE detectors are also found, to have significant performance gains over other well established interference cancellation techniques like the decorrelating detector, especially in heavily loaded system conditions. The implementation architecture of MMSE receivers can be either Multiple-Input Multiple Output (MIMO) or Single-Input Single-Output. The later offers not only complexity that is comparable to the conventional detector, but also has the inherent advantage of employing adaptive algorithms which can be used to provide both the dispreading and the interference cancellation function, without the knowledge of the codes of interfering users. Furthermore, in multipath fading channels, adaptive MMSE detectors can exploit the multipath diversity acting as RAKE combiners. The later ability is distinctive to MMSE based receivers, and it is achieved in an autonomous fashion, without the knowledge of the multipath intensity profile. The communicator achieves its performance objectives by the synergy of the signal processor and the channel decoder. According to the propositions of this thesis, the form of the signal processor needs to be changed, in order to exploit the horizons of spread spectrum signaling. However, maximum likelihood channel decoding algorithms need not change. It is the way that these algorithms are utilized that needs to be revis ed. In this respect, we identify three major utilization scenarios and an attempt is made to quantify which of the three best matches the requirements of a UMTS oriented CDMA radio interface. Based on our findings, channel coding can be used as a mapping technique from the information bit to a more ''intelligent" chip, matching the ''intelligence" of the signal processor

High capacity multiuser multiantenna communication techniques

One of the main issues involved in the development of future wireless communication systems is the multiple access technique used to efficiently share the available spectrum among users. In rich multipath environment, spatial dimension can be exploited to meet the increasing number of users and their demands without consuming extra bandwidth and power. Therefore, it is utilized in the multiple-input multiple-output (MIMO) technology to increase the spectral efficiency significantly. However, multiuser MIMO (MU-MIMO) systems are still challenging to be widely adopted in next generation standards. In this thesis, new techniques are proposed to increase the channel and user capacity and improve the error performance of MU-MIMO over Rayleigh fading channel environment. For realistic system design and performance evaluation, channel correlation is considered as one of the main channel impurities due its severe influence on capacity and reliability. Two simple methods called generalized successive coloring technique (GSCT) and generalized iterative coloring technique (GICT) are proposed for accurate generation of correlated Rayleigh fading channels (CRFC). They are designed to overcome the shortcomings of existing methods by avoiding factorization of desired covariance matrix of the Gaussian samples. The superiority of these techniques is demonstrated by extensive simulations of different practical system scenarios. To mitigate the effects of channel correlations, a novel constellation constrained MU-MIMO (CC-MU-MIMO) scheme is proposed using transmit signal design and maximum likelihood joint detection (MLJD) at the receiver. It is designed to maximize the channel capacity and error performance based on principles of maximizing the minimum Euclidean distance (dmin) of composite received signals. Two signal design methods named as unequal power allocation (UPA) and rotation constellation (RC) are utilized to resolve the detection ambiguity caused by correlation. Extensive analysis and simulations demonstrate the effectiveness of considered scheme compared with conventional MU-MIMO. Furthermore, significant gain in SNR is achieved particularly in moderate to high correlations which have direct impact to maintain high user capacity. A new efficient receive antenna selection (RAS) technique referred to as phase difference based selection (PDBS) is proposed for single and multiuser MIMO systems to maximize the capacity over CRFC. It utilizes the received signal constellation to select the subset of antennas with highest (dmin) constellations due to its direct impact on the capacity and BER performance. A low complexity algorithm is designed by employing the Euclidean norm of channel matrix rows with their corresponding phase differences. Capacity analysis and simulation results show that PDBS outperforms norm based selection (NBS) and near to optimal selection (OS) for all correlation and SNR values. This technique provides fast RAS to capture most of the gains promised by multiantenna systems over different channel conditions. Finally, novel group layered MU-MIMO (GL-MU-MIMO) scheme is introduced to exploit the available spectrum for higher user capacity with affordable complexity. It takes the advantages of spatial difference among users and power control at base station to increase the number of users beyond the available number of RF chains. It is achieved by dividing the users into two groups according to their received power, high power group (HPG) and low power group (LPG). Different configurations of low complexity group layered multiuser detection (GL-MUD) and group power allocation ratio (η) are utilized to provide a valuable tradeoff between complexity and overall system performance. Furthermore, RAS diversity is incorporated by using NBS and a new selection algorithm called HPG-PDBS to increase the channel capacity and enhance the error performance. Extensive analysis and simulations demonstrate the superiority of proposed scheme compared with conventional MU-MIMO. By using appropriate value of (η), it shows higher sum rate capacity and substantial increase in the user capacity up to two-fold at target BER and SNR values

Quasi-synchronous Random Access for Massive MIMO Based LEO Satellite Constellation

peer reviewedLow earth orbit (LEO) satellite constellationenabled communication network is considered to be an indispensable part to realize the Internet of Things (IoT) due to its unique advantages in providing seamless global coverage. In this paper, we investigate the random access problem in massive multipleinput multiple-output based LEO satellite communication systems. To deal with grant-free random access in IoT, a training sequence padded multi-carrier system is designed with tolerance to imperfect synchronization. Specifically, we construct a multisatellite system where a training sequence is utilized to perform joint activity detection and channel estimation (JADCE) at the edge satellite nodes. Considering the sparse feature of terrestrialsatellite link and sporadic transmission of user terminals (UTs), we propose a compressed sensing-based algorithm to estimate the delay tap and UT activities. To further utilize the structured feature of the receive array, a 2-D ESPRIT algorithm is performed for augmented parameterized channel estimation. Finally, enhanced activity detection and data detection are performed at the central node by leveraging the aggregated information from edge nodes. To achieve reliable information transmission, we propose a centralized interference cancellation and data detection method, where both the high spatial correlation among UTs and quantized backhaul are taken into account. Simulation results verify the effectiveness of our proposed scheme in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access satellite system