A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System

Abstract

This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i.e., sparse code multiple access (SCMA). HD-NOMA combines the code-domain and power-domain NOMA schemes by clustering the users in small path loss (strong) and large path loss (weak) groups. The two groups are decoded using successive interference cancellation while within the group users are decoded using the message passing algorithm. To further improve the performance of the system, a spectral-efficiency maximization problem is formulated under a user quality-of-service constraint, which dynamically assigns power and subcarriers to the users. The problem is non-convex and has sparsity constraints. The alternating optimization procedure is used to solve it iteratively. We apply successive convex approximation and reweighted 1 minimization approaches to deal with the non-convexity and sparsity constraints, respectively. The performance of the proposed HD-NOMA is evaluated and compared with the conventional SCMA scheme through numerical simulation. The results show the potential of HD-NOMA in increasing the number of uplink users that can be supported in a given time-frequency resource.

Beyond Equal-Power Sparse NOMA: Two User Classes and Closed-Form Bounds on the Achievable Region

“…A comparison to (13) can thus be used to identify settings where the outer bound of Proposition 2 is indeed useful. Note that ( 13) is also, in fact, the achievable region with a corresponding idealized SCMA scheme, where each user transmits independent Gaussian symbols over each of the utilized orthogonal resources; see, e.g., [20].…”

“…Accordingly, the corner points of the achievable region bounds correspond to a SIC scheme between the two user classes, while incorporating near-optimal joint iterative decoding (MPA) within each class. In fact, by this interpretation, our analysis provides, in a sense, an analytical benchmark for the setting considered in [ 20 ] (see also [ 18 ]), under the simplifying assumptions of non-fading channels and full symmetry among the users in each user class. Note that, in the absence of fading, the corresponding regular SCMA achievable sum rate, while assuming independent Gaussian signaling over each utilized resource, trivially coincides with the Cover–Wyner sum capacity (see, e.g., [ 43 ] and Section 3.3 ).…”

“…Transmission schemes combining power-domain NOMA and SCMA were also recently proposed, e.g., in [ 15 , 16 ] for the cellular downlink channel and in [ 17 , 18 , 19 , 20 , 21 ] for the uplink channel. Therein, the main objective is to identify efficient centralized algorithms for joint resource and power allocation, that attempt to maximize the SCMA achievable throughput under independent Rayleigh fading and certain simplifying assumptions (viz., independent Gaussian signaling over each utilized physical resource, full synchronization and perfect channel state information).…”

“…Therein, the main objective is to identify efficient centralized algorithms for joint resource and power allocation, that attempt to maximize the SCMA achievable throughput under independent Rayleigh fading and certain simplifying assumptions (viz., independent Gaussian signaling over each utilized physical resource, full synchronization and perfect channel state information). Fairness and quality-of-service constraints may also be incorporated into the optimization algorithm (e.g., [ 19 , 20 , 21 ]). The performance of the proposed algorithms is then evaluated by means of numerical simulations.…”

“…Another applicable use case is a single cell with users located at the extremes of either the cell center or the cell edge. Our analysis is also applicable to a compelling combination of power-domain NOMA [ 2 , 6 ] and code-domain NOMA, which has only recently started to attract attention in the literature [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ], as discussed above. Accordingly, the corner points of the achievable region bounds correspond to a SIC scheme between the two user classes, while incorporating near-optimal joint iterative decoding (MPA) within each class.…”