A Unified Graph Labeling Algorithm for Consecutive-Block Channel Allocation in SC-FDMA

• Published in: IEEE transactions on wireless communications Vol. 12; no. 11; pp. 5767 - 5779
• Main Authors: Lei, Lei, Yuan, Di, Ho, Chin Keong, Sun, Sumei
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm; Algorithm design and analysis; Algorithms; Applied sciences; Channel allocation; Complexity theory; Exact sciences and technology; Heuristic; Heuristic algorithms; Labeling; Operations research; optimization; Resource management; single carrier frequency division multiple access; Systems, networks and services of telecommunications; TECHNOLOGY; TEKNIKVETENSKAP; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); Uplink; Performance evaluation; Frequency division multiple access; Shortest path; Labelling; Single carrier; Electric utility; Algorithm; Optimization; Polynomial time; Carrier frequency; Optimal allocation; Optimal solution; Channel allocation; single carrier frequency division multiple access; Numerical simulation; Optimal path; Localization; Time complexity; Acyclic graph
• ISSN: 1536-1276; 1558-2248; 1558-2248
• DOI: 10.1109/TWC.2013.092313.130092

Optimal channel allocation is a key performance engineering aspect in single-carrier frequency-division multiple access (SC-FDMA). In SC-FDMA with localized channel assignment, the channels of each user must form a consecutive block. Subject to this constraint, various performance objectives, such as maximum utility, minimum power, and minimum number of channels, have been studied. We present a unified graph labeling algorithm for these problems, based on the structural insight that SC-FDMA channel allocation can be modeled as finding an optimal path in an acyclic graph. By this insight, our algorithm applies the concept of labeling and label domination that represent non-trivial extensions of finding a shortest or longest path. The key parameter in trading performance versus computation is the number of labels kept per node. Increasing the number ultimately enables global optimality. The algorithm's approach is further justified by its global optimality guarantee with strong polynomial-time complexity for two specific scenarios, where the input is user-invariant and channel-invariant, respectively. For the general case, we provide numerical results demonstrating the algorithm's ability of attaining near-optimal solutions.