Abstract
In this paper, we consider a reconfigurable intelligent surface (RIS) assisted rate splitting multiple access (RSMA) transmission system with estimated channel state information (CSI). The RIS is used to artificially construct the transmission environment to achieve more energy efficient transmission. An energy efficiency maximization problem is formulated by satisfying the constraint of power budget, the design principles of RSMA and RIS. To solve this problem, fractional programming is first used to decouple the single ratio objective function. Then the optimal power allocation coefficients and the phase shift matrix of RIS are obtained by the proposed alternative optimization method, respectively. Numerical results demonstrate that the energy efficiency performance of the RIS assisted RSMA system can be significantly improved by the proposed alternative joint optimization.
This work was supported in part by the NSF of Shandong Province under Grant ZR2021LZH010, Grant ZR2020LZH015, and Grant ZR2020MF042; and in part by the NSF of China under Grant U1736122 and Grant 62071005.
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References
Zhou, G., Pan, C., Ren, H., Wang, K., Nallanathan, A.: Intelligent reflecting surface aided multigroup multicast MISO communication systems. IEEE Trans. Signal Process. 68, 3236–3251 (2020)
Pan, C., Ren, H., Wang, K., Xu, W., Hanzo, L.: Multicell MIMO communications relying on intelligent reflecting surfaces. IEEE Trans. Wireless Commun. 19(8), 5218–5233 (2020)
Huang, C., Zappone, A., Alexandropoulos, G.C., Debbah, M., Yuen, C.: Reconfigurable intelligent surfaces for energy efficiency in wireless communication. IEEE Trans. Wireless Commun. 18(8), 4157–4170 (2019)
Joudeh, H., Clerckx, B.: Rate-splitting for max-min fair multigroup multicast beamforming in overloaded systems. IEEE Trans. Wireless Commun. 16(11), 7276–7289 (2017)
Mao, Y., Clerckx, B., Li, V.O.K.: Rate-splitting multiple access for downlink communication systems: bridging, generalizing, and outperforming SDMA and NOMA. EURASIP J. Wirel. Commun. Network. 2018, 133 (2018)
Chang, Z., Ristaniemi, T.: Energy efficiency of unicast support multicast with QoS guarantee. In: 2013 IEEE/CIC International Conference on Communications in China - Workshops (CIC/ICCC), Xi’an, China, pp. 16–20 (2013). https://doi.org/10.1109/ICCChinaW.2013.6670559
Al-Oquibi, B., Amin, O., Dahrouj, H., Al-Naffouri, T.Y., Alouini, M.: Energy efficiency for cloud-radio access networks with imperfect channel state information. In: 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, Spain, pp. 1–5 (2016). https://doi.org/10.1109/PIMRC.2016.7794612
Zhao, M., Zhao, J., Zhou, W., Zhu, J., Zhang, S.: Energy efficiency optimization in relay-assisted networks with energy harvesting relay constraints. China Commun. 12(2), 84–94 (2015)
Mao, Y., Clerckx, B., Li, V.O.K.: Energy efficiency of rate-splitting multiple access, and performance benefits over SDMA and NOMA. In: 2018 15th International Symposium on Wireless Communication Systems (ISWCS), Lisbon, Portugal, pp. 1–5 (2018). https://doi.org/10.1109/ISWCS.2018.8491100
Du, L., Huang, C., Guo, W., Ma, J., Ma, X.: Reconfigurable intelligent surfaces assisted secure multicast communications. IEEE Wirel. Commun. Lett. 9(10), 1673–1676 (2020)
Shen, H., Xu, W., Gong, S., He, Z., Zhao, C.: Secrecy rate maximization for intelligent reflecting surface assisted multi-antenna communications. IEEE Commun. Lett. 23(9), 1488–1492 (2019)
Wang, Y., Lu, H., Zhao, D., Sun, H.: Energy efficiency optimization in IRS-enhanced mmWave systems with lens antenna array. In: 2020 IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, pp. 1–6 (2020). https://doi.org/10.1109/GLOBECOM42002.2020.9348266
You, L., Xiong, J., Ng, D.W.K., Yuen, C., Wang, W., Gao, X.: Energy efficiency and spectral efficiency tradeoff in RIS-aided multiuser MIMO uplink transmission. IEEE Trans. Signal Process. 69, 1407–1421 (2021)
Jin, Y., Zhang, J., Huang, C., Yang, L., Xiao, H., Ai, B.: Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation. IEEE Trans. Veh. Technol. 71(2), 2134–2139 (2022)
Shao, X., Cheng, L., Chen, X., Huang, C., Kwan Ng, D.W.: A Bayesian tensor approach to enable RIS for 6G massive unsourced random access. In: 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, pp. 1–7 (2021). https://doi.org/10.1109/GLOBECOM46510.2021.9685371
Zhang, J., Qi, C., Li, P., Lu, P.: Channel estimation for reconfigurable intelligent surface aided massive MIMO system. In: 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Atlanta, GA, USA, pp. 1–5 (2020). https://doi.org/10.1109/SPAWC48557.2020.9154276
Jin, Y., Zhang, J., Zhang, X., Xiao, H., Ai, B., Ng, D.W.K.: Channel estimation for semi-passive reconfigurable intelligent surfaces with enhanced deep residual networks. IEEE Trans. Veh. Technol. 70(10), 11083–11088 (2021)
Shen, K., Yu, W.: Fractional programming for communication systems-Part I: power control and beamforming. IEEE Trans. Signal Process. 66(10), 2616–2630 (2018)
Du, L., Zhang, W., Ma, J., Tang, Y.: Reconfigurable intelligent surfaces for energy efficiency in multicast transmissions. IEEE Trans. Veh. Technol. 70(6), 6266–6271 (2021)
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Gao, C., Zhang, J., Guo, L., Meng, L., Ji, H., Sun, J. (2022). Energy Efficiency Optimization for RIS Assisted RSMA System over Estimated Channel. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_53
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