Joint Optimization Methods for Nonconvex Resource Allocation Problems of Decode-and-Forward Relay-Based OFDM Networks

Abstract

Generally, resource allocation for multi-carrier cooperation communication networks include subcarrier and power allocation, however, it is difficult to be solved because of the 0-1 integer programming of subcarrier allocation, which makes the problem non-convex. This paper focus on solving the non-convex problems and provides a general solution to the resource allocation for relay enhanced multi-carrier systems. An established scenario, decode-and-forward (DF) relay assisted orthogonal frequency division multiplex (OFDM) system, is considered and we formulate the resource allocation as a joint subcarrier pairing, assignment and power allocation problem, in which heterogeneous users data rate requirements are also considered. In order to make the expression of the proposed optimization approaches for the original maximization problem clear, a four-step methodology is given. Firstly, we transform the original non-convex problem into a standard convex problem by imposing a new constraint of subcarrier allocation index regardless of the convexity of the objective function. Further, we prove that the optimum resource allocation algorithm (ORAA) for the dual problem of the transformed optimization problem is equivalent to the optimization of the original function. Subsequently, the optimum solution could be obtained by golden section search method (GSSM) and iterative resource allocation algorithm. Lastly, a sub-optimal resource allocation algorithm (SRAA) that solves the primal problem in an asymptotically manner is proposed. Simulation results illustrate that our proposed SRAA achieves a comparable performance as the ORAA with a satisfied complexity.

Symbol error probability optimisation in three‐phase bidirectional amplify‐and‐forward relaying systems

“…Given the optimal power allocation, only the split factor variable is left in the problem given by where has been applied in problem (25). As there is only one variable in problem (25), some prevailing one‐dimensional searches can be used to solve problem (25), such as the golden section method and the Fibonacci method [16].…”

Resource allocation strategy against selfishness in cognitive radio ad‐hoc network based on Stackelberg game

“…1b, PB chooses the active and suitable SUs which satisfy the relay selection condition, the active and suitable SUs relay the data received in the first sub-slot to PR collectively. Since OFDMA technique can make full use of spectrum resource without interference [27], the OFDMA technique is used by the active and suitable SUs, specifically, each SU i has the ability to detect the surrounding environment, and dynamically adjust the used spectrum according to OFDMA technique. Therefore, the interference between SUs can be ignored in the second and third sub-slot time.…”

“…Similar to [27], the relay-aided OFDMA system is assumed in this paper, the available bandwidth is divided into some subcarriers, and the subcarriers are orthogonal with each other. Since each SU i has the different available subcarriers which are all orthogonal, no interference exists between SUs.…”

Embedded protocols based on the crowd Petri networks and opportunistic bandwidth allocation

“…Based on the above research results such as Joint Optimization [6], efficient distributed resource allocation [7], online network selection algorithm [9], and Transition cover-based design of Petri net controllers [11], combined with our previous research results [17], we proposed the embedded protocols based on the crowd Petri networks with the opportunistic bandwidth allocation.…”

“…The Joint Optimization Methods was proposed in [6], which is able to solve the non-convex problems and provides a general solution to the resource allocation for relay enhanced multi-carrier systems. Aristomenopoulos G et al [7] studied the efficient-distributed resource allocation and users to cells assignment over a heterogeneous integrated wireless environment.…”