TY - GEN
T1 - Beam-Based Resource Allocation in THz-NOMA Networks
AU - Ding, Zhiguo
AU - Poor, H. Vincent
N1 - Funding Information:
formulated as a mixed combinatorial non-convex optimization problem, and then solved by two methods, one based on the BB method and the other based on SCA. VII. ACKNOWLEDGEMENTS Z. Ding was supported by H2020 H2020-MSCA-RISE-2020 under grant number 101006411. H. V. Poor was supported by the U.S National Science Foundation under Grants CCF-1908308 and CNS-2128448. REFERENCES
Publisher Copyright:
© VDE VERLAG GMBH · Berlin · Offenbach.
PY - 2023
Y1 - 2023
N2 - This paper investigates how to apply non-orthogonal multiple access (NOMA) as an add-on in terahertz (THz) networks. In particular, prior to the implementation of NOMA, it is assumed that there exists a legacy THz system, where spatial beams have already been configured to serve legacy primary users. The aim of this paper is to study how these pre-configured spatial beams can be used as a type of bandwidth resources, on which additional secondary users are served without degrading the performance of the legacy primary users. A joint beam management and power allocation problem is first formulated as a mixed combinatorial non-convex optimization problem, and then solved by two methods with different performancecomplexity tradeoffs, one based on the branch and bound method and the other based on successive convex approximation.
AB - This paper investigates how to apply non-orthogonal multiple access (NOMA) as an add-on in terahertz (THz) networks. In particular, prior to the implementation of NOMA, it is assumed that there exists a legacy THz system, where spatial beams have already been configured to serve legacy primary users. The aim of this paper is to study how these pre-configured spatial beams can be used as a type of bandwidth resources, on which additional secondary users are served without degrading the performance of the legacy primary users. A joint beam management and power allocation problem is first formulated as a mixed combinatorial non-convex optimization problem, and then solved by two methods with different performancecomplexity tradeoffs, one based on the branch and bound method and the other based on successive convex approximation.
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M3 - Conference contribution
AN - SCOPUS:85166920010
T3 - WSA and SCC 2023 - 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding
SP - 338
EP - 343
BT - WSA and SCC 2023 - 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding
PB - VDE Verlag GmbH
T2 - 26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on Systems, Communications, and Coding, SCC 2023
Y2 - 27 February 2023 through 3 March 2023
ER -