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QoE-Aware Resource Allocation for Non-Orthogonal Multiple Access Enhanced HetNets
IEEE Transactions on Cognitive Communications and Networking  (IF4.341),  Pub Date : 2021-04-22, DOI: 10.1109/tccn.2021.3074944
Liangyu Chen, Bo Hu, Shanzhi Chen, Jianpeng Xu, Guixian Xu

Non-orthogonal multiple access (NOMA) has drawn significant attention due to its high spectral efficiency. Invoking NOMA in heterogeneous networks (HetNets) can support ubiquitous connectivity and satisfy the growing demand for mobile data traffic. Existing studies on NOMA-enhanced HetNets mainly focus on network’s quality of service (QoS) metrics such as delay, throughput, coverage, etc. However, these parameters are not sufficient for evaluating the quality of experience (QoE) perceived by users. To that end, we propose a QoE-aware resource allocation framework for NOMA-enhanced HetNets under Web browsing and video services. Specifically, a unified QoE-aware joint subchannel and power allocation optimization problem is formulated to maximize the sum mean opinion scores (MOSs) of all users, while guaranteeing the QoE requirement of each user. However, this problem is mixed-integer, non-convex, and intractable. To solve it, a penalty-based iterative algorithm is proposed. In particular, binary constraints on subchannel assignment variables are equivalently transformed into equality constraints via penalty method. Then, subchannel assignment and power allocation are alternately optimized in each iteration by leveraging block coordinate descent method and sequential parametric convex approximation techniques. Extensive numerical results show that the proposed scheme could achieve competitive QoE performance compared to existing NOMA and orthogonal multiple access schemes.