Resource Allocation via Max–Min Goodput Optimization for BIC-OFDMA Systems

التفاصيل البيبلوغرافية
العنوان: Resource Allocation via Max–Min Goodput Optimization for BIC-OFDMA Systems
المؤلفون: Tao Wang, Filippo Giannetti, Vincenzo Lottici, Riccardo Andreotti, Luc Vandendorpe
المصدر: IEEE Transactions on Communications. 64:2412-2426
بيانات النشر: Institute of Electrical and Electronics Engineers (IEEE), 2016.
سنة النشر: 2016
مصطلحات موضوعية: Mathematical optimization, ant colony optimization, Optimization problem, Computer science, Automatic repeat request, Goodput, Orthogonal frequency-division multiple access, Retransmission, bit-interleaved coded modulation, resource allocation, max-min optimization problem, 02 engineering and technology, Subcarrier, 0203 mechanical engineering, Telecommunications link, Orthogonal frequency division multiple access (OFDMA), bit-interleaved coded modulation, automatic repeat request (ARQ), goodput, resource allocation, max-min optimization problem, ant colony optimization, automatic repeat request (ARQ), 0202 electrical engineering, electronic engineering, information engineering, Resource management, Electrical and Electronic Engineering, Metaheuristic, goodput, Quality of service, Ant colony optimization algorithms, 020302 automobile design & engineering, 020206 networking & telecommunications, Orthogonal frequency division multiple access (OFDMA), Resource allocation, Performance metric
الوصف: In this paper, a novel resource allocation (RA) strategy is designed for the downlink of orthogonal frequency division multiple access networks employing practical modulation and coding under quality of service constraints and retransmission techniques. Compared with previous works, two basic concepts are combined together, namely: 1) taking the goodput (GP) as performance metric and 2) ensuring maximum fairness among users. Thus, the resulting RA maximizes the GP of the worst users, optimizing subcarrier allocation (SA), per-subcarrier power allocation (PA), and adaptation of modulation and coding (AMC) of the active users, yielding a nonlinear nonconvex mixed optimization problem (OP). The intrinsic demanding difficulty of the OP is tackled by iteratively and optimally solving the AMC, PA, and SA subproblems, devoting special care to the difficult nonlinear combinatorial SA-OP. First, the optimal (yet computationally complex) solution is found by applying the branch and bound method to the optimal SA solution found in the relaxed domain, and accordingly, it is taken as benchmark. Then, an innovative suboptimal yet efficient solution based on the metaheuristic ant colony optimization (ACO) framework is derived. The proposed RA strategy is corroborated by comprehensive simulations, showing improved performance even at the cost of affordable numerical complexity.
تدمد: 0090-6778
DOI: 10.1109/tcomm.2016.2555311
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b386814858fb8728def193d71499edf
https://doi.org/10.1109/tcomm.2016.2555311
Rights: OPEN
رقم الانضمام: edsair.doi.dedup.....0b386814858fb8728def193d71499edf
قاعدة البيانات: OpenAIRE
الوصف
تدمد:00906778
DOI:10.1109/tcomm.2016.2555311