Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems

Abstract

Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems.

Hybrid OFDMA Resource Allocation Scheme for Ensuring Required Level of Proportional Fairness

“…With the help of the fitness function (7), the ABC in [26,27] is more likely to obtain higher sum data rate. is is because that the sum data rate has more impact on the fitness function (7) than the index of the proportional fairness F. As a result, the ABC with the fitness function (7) would not Based on a principle that (p k,n + 1/G k,n ) equals to (p k,m + 1/G k,m ) as n ≠ m, the strict proportional fairness (i.e., F � 1) can be achieved by a power allocation algorithm in [5]. However, in order for the strict proportional fairness, numerical algorithms like the Newton-Raphson method should be applied to solve a complex nonlinear equation.…”

“…Resource allocation involved in OFDMA, nonorthogonal multiple access (NOMA), and rate splitting multiple access (RSMA) have been an active research aspect in wireless communication systems, and various techniques such as greedy algorithms, evolutionary algorithms, and artificial intelligent algorithms have been applied for resource allocation . In the downlink of an OFDMA net cell, rate adaptive optimization is to maximize the sum data rate transmitted to users by a base station with the constraint of the total transmit power of the base station [5], and the resource allocation is referred to as the base station allocation of subcarriers and power to users. Joint subcarrier and power allocation, i.e., simultaneous optimization of subcarrier and power, tend to be computationally intensive because of its NP-hard nature [6,7].…”

“…Joint subcarrier and power allocation, i.e., simultaneous optimization of subcarrier and power, tend to be computationally intensive because of its NP-hard nature [6,7]. As a result, separate subcarrier and power allocation or even subcarrier allocation with equal power allocation is more likely to be used because of the lower computational complexity [5].…”

“…As one of the QoS indices, the proportional fairness among users, which is predetermined by a proportional data rate constraint [5], can be used to ensure each user to obtain the required data rate. Hence, maximizing the sum data rate and simultaneously ensuring the proportional fairness among users draw extensive and increasing attention persistently.…”

“…where F is the index of the proportional data rate fairness [5,7,15]; ε is the required level of proportional fairness; and λ k is the predetermined data rate proportionality value of user k. e index F varies from 0 to 1. Note that a larger index F indicates a better proportional fairness among users.…”