Non-Orthogonal Multiple Access (NOMA) and Fog Radio Access Networks (FRAN) are promising candidates within the 5G and beyond systems. This work examines the benefit of adopting NOMA in an FRAN architecture with constrained capacity fronthaul. The paper proposes methods for optimizing joint scheduling and power adaptation in the downlink of a NOMA-based FRAN with multiple resource blocks (RB). We consider a mixed-integer optimization problem which maximizes a network-wide rate-based utility function subject to fronthaul-capacity constraints, so as to determine i) the user-to-RB assignment, ii) the allocated power to each RB, and iii) the power split levels of the NOMA users in each RB. The paper proposes a feasible decoupled solution for such non-convex optimization problem using a three-step hybrid centralized distributed approach. The proposed solution complies with FRAN operation that aims to partially shift the network control to the FAPs, so as to overcome delays due to fronthaul rate constraints. The paper proposes and compares two distinct methods for solving the assignment problem, namely the Hungarian method, and the Multiple Choice Knapsack method. The power allocation and the NOMA power split optimization, on the other hand, are solved using the alternating direction method of multipliers (ADMM). Simulations results illustrate the advantages of the proposed methods compared to different baseline schemes including the conventional Orthogonal Multiple Access (OMA), for different utility functions and different network environments.
The integration of NOMA in a CRAN architecture is analyzed in few recent works, e.g., @cite_9 @cite_18 @cite_31 @cite_26 @cite_23 @cite_6 @cite_3 @cite_14 . References @cite_9 and @cite_18 analyze the outage probability in a NOMA-based CRAN network. In particular, reference @cite_18 proposes an architecture, where the cell-center users are served by their nearest RRHs as their strong users, while multiple RRHs collaborate to serve the cell-edge users as their weak users. Using stochastic geometric tools, it is shown in @cite_18 that NOMA enhances the performance of the cell-edge users. References @cite_31 and @cite_26 analyze the energy efficiency of the DL of a CRAN cellular network. In @cite_31 , NOMA is adopted in the wireless backhaul links connecting the BSs to the cloud, while in @cite_26 , it is adopted in the communication between RRHs and users. Both papers show that the NOMA scheme can achieve higher energy efficiency compared to the conventional OMA.
@cite_18 @cite_26 @cite_14 @cite_9 @cite_6 @cite_3 @cite_23 @cite_31
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In this paper, a novel non-orthogonal multiple access (NOMA)-enabled framework for cloud-radio access networks (CRANs) is proposed. In this framework, two users are scheduled in the same resources according to NOMA; however the performance of cell-edge users is enhanced by means of coordinating beamforming. Stochastic geometry is invoked for modeling the proposed framework, where the positions of BSs follow a cluster point process. In an effort to characterize the performance of the proposed framework, simple expressions in terms of outage probability are derived for both nearby users and the cell-edge users. It is analytically demonstrated that the average beamforming gain is a liner function with respect to the number of cooperating base stations. Numerical results verify the accuracy of analysis and reveal that the proposed framework is capable of greatly enhancing the performance of cell-edge users. The number of wireless devices is growing rapidly on a daily basis echoing the increasing number of applications of the Internet of Thing. Facing massive connections and unavoidable interference, how to provide a green communication is a concerning matter. In this regard, nonorthogonal multiple-access (NOMA) is a natural communications technology that can scale with the massive number of simultaneous connections for a limited bandwidth. In this paper, we aim to maximize the energy efficiency (EE) for an NOMA-based cloud radio access network, where sub-6 GHz and millimeter wave bands are used in fronthaul and access links, respectively. In particular, we formulate the power optimization problem to maximize the EE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form. A two-layer algorithm is then proposed. In the outer loop, the @math -norm technique is adopted to transform the nonconvex fronthaul capacity constraint into a convex one, whereas in the inner loop, the weighted minimum mean square error approach is applied. Simulation results indicate that the proposed NOMA scheme can obtain higher EE as well as throughput when compared with orthogonal multiple-access methods. This paper studies the application of non-orthogonal multiple access (NOMA) to cache-enabled cloud radio access network (CRAN) with mixed multicast and unicast transmission. Users requesting the same content are grouped together and served with a cluster of remote radio heads (RRHs) using distributed beamforming. In addition, the user with better channel condition in each group is allowed to request an extra unicast content via the NOMA protocol. Each RRH has a local cache which enables it to acquire the requested contents either from the local cache or from the central processor via the fronthaul link. Taking the maximum fronthaul capacity into consideration, we investigate the subchannel (SC) allocation problem to both RRHs and multicast groups to improve the weighted network sum rate. The optimal solution requires exhaustive search, which become prohibitively complicated as the number of RRHs and groups increases. To tackle this problem effectively, we formulate this problem as a three-sided matching problem among SCs, RRHs and multicast groups, and propose a novel low-complexity matching algorithm. We prove mathematically that the proposed algorithm converges to a stable matching within limited number of iterations. Numerical results unveil that the proposed algorithm closely approaches the optimal solution and outperforms the conventional orthogonal multiple access (OMA)-based CRAN. To improve system capacity and spectral efficiency, as well as support more connections in cloud radio access networks (C-RANs), the application of non-orthogonal multiple access (NOMA), a promising technique in the physical layer, is considered in this letter. The stochastic geometry is used to obtain the outage probability of NOMA-based downlink C-RAN, in which the remote radio heads are uniformly distributed and serve two paired users simultaneously. A closed-form approximation for the outage probability is derived, and Monte Carlo simulations are provided to validate the accuracy of the theoretical analysis. Compared with conventional multiple access techniques, both the obtained analytical and simulation results demonstrate that the proposed NOMA scheme can offer better spectral efficiency and user fairness in C-RANs. This paper investigates multiple access techniques for wireless downlink in cloud radio access networks (CRANs) where all base stations (BSs) are connected via a cloud. We first propose a power allocation scheme that makes use of non-orthogonal multiple access to allocate the power at the BSs based on their relative wireless distances to the cloud. Specifically, a successive interference cancellation mechanism is designed at the cloud to lay multiple BSs over each other in the power domain. We then analyse the achievable throughput of the NOMA in the CRANs. The derived expressions not only show the effectiveness of the proposed NOMA over the conventional orthogonal frequency division multiple access (OFDMA) but also allow us to develop an algorithm to find the optimal number of BSs in the CRANs. Finally, numerical results are provided to show an improvement of up to eight times of the sum data rate and a higher number of BSs that could be maintained with the proposed NOMA over the OFDMA scheme as well as evaluating the impact of the wireless propagation environment and channel quality on the system throughput and cloud-edge throughput performance.