In this paper, we propose a cross layer energy efficient resource allocation and remote radio head (RRH) selection algorithm for heterogeneous traffic in power domain-non-orthogonal multiple access (PD-NOMA) based heterogeneous cloud radio access networks (H-CRANs). The main aim is to maximize the EE of the elastic users subject to the average delay constraint of the streaming users and the constraints, RRH selection, subcarrier, transmit power, and successive interference cancellation. The considered optimization problem is non-convex, NP-hard, and intractable. To solve this problem, we transform the fractional objective function into a subtractive form. Then, we utilize successive convex approximation approach. Moreover, in order to increase the processing speed, we introduce a framework for accelerating the successive convex approximation for low complexity with the Lagrangian method on graphics processing unit. Furthermore, in order to show the optimality gap of the proposed successive convex approximation approach, we solve the proposed optimization problem by applying an optimal method based on the monotonic optimization. Studying different scenarios show that by using both PD-NOMA technique and H-CRAN, the system energy efficiency is improved.

In this paper, the tradeoff among the energy efficiency, fairness, harvested energy, and system sum rate is studied. In this regard, various fairness methods, namely, max-min fairness, proportional fairness, and minimum delay potential fairness in power-domain non-orthogonal multiple access-based heterogeneous cellular networks are investigated. In order to perform successive interference cancellation (SIC), we use two ordering approaches and compare their performance. To this end, we propose joint subcarrier and power allocation algorithms to achieve fair energy efficient resource allocation for each fairness method and SIC ordering. Since the proposed optimization problems are non-convex and intractable, the existing methods to solve the convex problems could not be directly used. To overcome this difficulty, an iterative algorithm based on successive convex approximation is used. Moreover, to show the optimality gap of the proposed solution method, an optimal approach based on the monotonic optimization is applied in which we first transform each of the proposed optimization problems into a monotonic optimization problem of canonical form, and then, we obtain the optimal solution of each problem, which coincides with the optimal solution of the original non-convex problem. We finally study the performance of the proposed schemes using simulations for different values of the system parameters.

In this study, the authors consider the downlink radio resource allocation for heterogeneous traffic in generalised frequency division multiplexing (GFDM)‐non‐orthogonal multiple access (NOMA) based heterogeneous cellular networks. In this scheme, multiple number of users can be allocated on each subcarrier. Two types of traffic are considered, elastic and streaming. The problem of maximising the weighted sum‐rate of elastic users is addressed subject to streaming users minimum rate in addition to subcarrier and transmit power constraints. This problem is a non‐convex NP‐hard optimisation problem. To solve this problem, the authors divide it into two subproblems, subcarrier allocation and power allocation then an iterative algorithm is proposed. Subcarrier allocation is updated by solving an integer linear program, where a successive convex approximation approach is adopted to transform the power allocation subproblem to a sequence of convex subproblems, using one of the three methods, successive convex approximation for low ComplExity, arithmetic‐geometric mean approximation (AGMA) and difference of two concave functions to find the power allocation optimal solutions. Numerical experiments show that the proposed algorithms can improve the system performance. Furthermore, they show that AGMA can achieve a sum‐rate near to the global optimal solution, at the expense of more computational time.

UNLABELLED: RNA atomic resolution structures have revealed the existance of different families of basepair interactions, each of which with its own isosteric sub-families. Ribostral (Ribonucleic Structural Aligner) is a user-friendly framework for analyzing, evaluating and viewing RNA sequence alignments with at least one available atomic resolution structure. It is the first of its kind that makes direct and easy- to-understand superposition of the isostericity matrices of basepairs observed in the structure onto sequence alignments, easily indicating allowed and unallowed substitutions at each BP position. Potential mistakes in the alignments can then be corrected using other sequence editing software. Ribostral has been developed and tested under Windows XP, and is capable of running on any PC or MAC platform with MATLAB 7.1 (SP3) or higher installed version. A stand-alone version is also available for the PC platform. AVAILABILITY: http://rna.bgsu.edu/ribostral.