Yin Zhendong, Zhuang Shufeng, Wu Zhilu, Ma Bo
School of Electronics and Information Engineering, Harbin Institute of Technology, 92 Xidazhi Street, Harbin 150001, China.
Sensors (Basel). 2015 Sep 25;15(10):24996-5014. doi: 10.3390/s151024996.
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.
正交频分多址接入（OFDMA）在无线传感器网络中被广泛应用，它允许不同用户根据其子信道增益获得不同的子载波。因此，如何为不同用户分配子载波和功率以实现高系统总速率是OFDMA系统中的一个重要研究领域。本文的研究重点是具有比例公平性约束的基于速率自适应（RA）的资源分配。由于资源分配是一个NP难的非凸优化问题，提出了一种新的高效资源分配算法ACO-SPA，它结合了蚁群优化（ACO）和次优功率分配（SPA）。为了降低计算复杂度，将OFDMA系统中资源分配的优化问题分为两步。第一步，执行蚁群优化算法来解决子载波分配问题。然后，基于不同子信道中每个用户的功率和信道噪声比倒数之和相等的原则，开发了具有严格比例公平性的次优功率分配算法。为支持该算法，给出了大量的仿真结果。与求根法和线性方法相比，该方法在解决OFDMA系统中的比例资源分配问题时具有更好的性能。
