Non-orthogonal multiple access (NOMA) schemes serve more than one user in the same resource block by multiplexing users in other domains than frequency and time. In this way, NOMA schemes offer several advantages over orthogonal multiple access (OMA) schemes such as improved user fairness and spectral efficiency, higher cell-edge throughput, massive connectivity support, and low transmission latency. With these merits, NOMA transmission schemes are being increasingly looked at as a promising multiple access scheme for future wireless networks. When the power domain is used to multiplex users, it is referred to as the power domain NOMA (PD-NOMA) scheme. In this paper, we survey the integration of the PD-NOMA scheme with other upcoming communication schemes and technologies that satisfy the requirements of 5G and beyond 5G (B5G) networks. In particular, this paper surveys the rate optimization schemes studied in the literature when the PD-NOMA scheme is combined with MIMO and massive MIMO (mMIMO), millimeter wave (mmWave) communications, coordinated multi-point (CoMP) transmission and reception, cooperative communications, cognitive radio (CR), visible light communications (VLC), and unmanned aerial vehicle (UAV) assisted communications. The considered system models, the optimization methods used to maximize the achievable rates, and the main outcomes on the performance of these NOMA-enabled schemes are discussed along with future research directions for these combined schemes.
This paper proposes a millimeter wave-NOMA (mmWave-NOMA) system that takes into account the end-user signal processing capabilities, an important practical consideration. The implementation of NOMA in the downlink (DL) direction requires successive interference cancellation (SIC) to be performed at the user terminals, which comes at the cost of additional complexity. In NOMA, the weakest user only has to decode its own signal, while the strongest user has to decode the signals of all other users in the SIC procedure. Hence, the additional implementation complexity required of the user to perform SIC for DL NOMA depends on its position in the SIC decoding order. Beyond fifth-generation (B5G) communication systems are expected to support a wide variety of end-user devices, each with their own processing capabilities. We envision a system where users report their SIC decoding capability to the base station (BS), i.e., the number of other users signals a user is capable of decoding in the SIC procedure. We investigate the rate maximization problem in such a system, by breaking it down into a user clustering and ordering problem (UCOP), followed by a power allocation problem. We propose a NOMA-minimum exact cover (NOMA-MEC) heuristic algorithm that converts the UCOP into a cluster minimization problem from a derived set of valid cluster combinations after factoring in the SIC decoding capability. The complexity of NOMA-MEC is analyzed for various algorithm and system parameters. For a homogeneous system of users that all have the same decoding capabilities, we show that this equates to a simple maximum number of users per cluster constraint and propose a lower complexity NOMA-best beam (NOMA-BB) algorithm. Simulation results demonstrate the performance superiority in terms of sum rate compared to orthogonal multiple access (OMA) and traditional NOMA clustering schemes that do not incorporate individual users' SIC decoding capability constraints. INDEX TERMS Non-orthogonal multiple access (NOMA), millimeter-wave (mmWave), User clustering (UC), Successive interference cancellation (SIC), Minimum exact cover (MEC) problem.
Sparse code multiple access (SCMA) is a non-orthogonal multiple access (NOMA) uplink solution that overloads resource elements (RE's) with more than one user. Given the success of orthogonal frequency division multiplexing (OFDM) systems, SCMA will likely be deployed as a multiple access scheme over OFDM, called an SCMA-OFDM system. One of the major challenges with OFDM systems is the high peak-to-average power ratio (PAPR) problem, which is typically studied through the PAPR statistics for a system with a large number of independently modulated sub-carriers (SCs). In the context of SCMA systems, the PAPR problem has been studied before through the SCMA codebook design for certain narrowband scenarios, applicable more for low-rate users. However, we show that for high-rate users in wideband systems, it is more meaningful to study the PAPR statistics. In this paper, we highlight some novel aspects to the PAPR statistics for SCMA-OFDM systems that is different from the vast body of existing PAPR literature in the context of traditional OFDM systems. The main difference lies in the fact that the SCs are not independently modulated in SCMA-OFDM systems. Instead, the SCMA codebook uses multi-dimensional constellations, leading to a statistical dependency between the data carrying SCs. Further, the SCMA codebook dictates that an UL user can only transmit on a subset of the available SCs. We highlight the joint effect of the two major factors that influence the PAPR statistics-the phase bias in the multi-dimensional constellation design along with the resource allocation strategy. The choice of modulation scheme and SC allocation strategy are static configuration options, thus allowing for PAPR reduction opportunities in SCMA-OFDM systems through the setting of static configuration parameters. Compared to the class of PAPR reduction techniques in the OFDM literature that rely on multiple signalling and probabilistic techniques, these gains come with no computational overhead. In this paper, we also examine these PAPR reduction techniques and their applicability to SCMA-OFDM systems. INDEX TERMS Sparse code multiple access (SCMA), peak-to-average power ratio (PAPR), orthogonal frequency division multiplexing (OFDM), sub-carrier (SC), uplink (UL), selective mapping (SLM), interleaving (IL).
Orthogonal frequency division multiplexing (OFDM) continues to be deployed in 5G communication systems and is likely to be used in beyond 5G (B5G) communication systems as well, due to its many advantages. However, one major drawback with OFDM systems is its high peak-to-average power ratio (PAPR), especially with large bandwidth transmissions. In this paper, we have provided a regularization optimization based flexible hybrid companding and clipping scheme (ROFHCC) used for PAPR reduction in OFDM systems. To reduce the design complexity, the companding function has two parts. It restrains the signal samples with amplitudes over a given value to a constant value for both peak power reduction and small power compensation. For signals with samples less than a given amplitude, they are expanded by a linear companding function. We build a regularization optimization model to jointly optimize the companding distortion as well as the continuity of the companding function for bit error rate (BER) performance as well as power spectral density (PSD) performance. Simulation results indicate that for the same PAPR performance, the proposed companding scheme has an advantage over the referenced companding schemes. For example, when the average signal power is normalized to be 1, we choose both PAPR for ROFHCC scheme and two-piecewise companding (TPWC) scheme as 4 dB, then we can find that at BER = 10 −4 , the minimum required E b /N 0 for ROFHCC scheme is around 2.3 dB lower than TPWC scheme.
The various requirements in terms of data rates and latency in beyond 5G and 6G networks have motivated the integration of a variety of communications schemes and technologies to meet these requirements in such networks. Among these schemes are Terahertz (THz) communications, cooperative non-orthogonal multiple-access (NOMA)-enabled schemes, and mobile edge computing (MEC). THz communications offer abundant bandwidth for high-data-rate short-distance applications and NOMA-enabled schemes are promising schemes to realize the target spectral efficiencies and low latency requirements in future networks, while MEC would allow distributed processing and data offloading for the emerging applications in these networks. In this paper, an energy-efficient scheme of multi-user NOMAassisted cooperative THz single-input multiple-output (SIMO) MEC systems is proposed to allow the uplink transmission of offloaded data from the far cell-edge users to the more computing resources in the base station (BS) through the cell-center users.To reinforce the performance of the proposed scheme, two optimization problems are formulated and solved, namely, the first problem minimizes the total users' energy consumption while the second problem maximizes the total users' computation energy efficiency (CEE) for the proposed scheme. In both problems, the NOMA user pairing, the BS receive beamforming, the transmission time allocation, and the NOMA transmission power allocation coefficients are optimized, while taking into account the full-offloading requirements of each user as well as the predefined latency constraint of the system. The obtained results reveal new insights into the performance and design of multi-user NOMAassisted cooperative THz-SIMO MEC systems. Particularly, with relatively high offloading rate demands (several Gbits/user), we show that (i) the proposed scheme can handle such demands while satisfying the predefined latency constraint, and (ii) the fulloffloading model can be considered the most effective solution in conserving mobile devices' resources as compared to the system with the partial-offloading model or the system without offloading.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.