Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks (2310.17868v2)
Abstract: Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily depends on the channel characteristics of the selected users, eliminating interference between users through beamforming, power control, etc. The above resource allocation issue constitutes a complex joint multi-objective optimization problem since many variables and parameters must be optimized, including the spatial degree of freedom, rate, power allocation, and transmission technique. In this article, we review the basic properties of near-field communications and focus on the corresponding "resource allocation" problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation.
- Z. Wang, J. Zhang, H. Du, D. Niyato, S. Cui, B. Ai, M. Debbah, K. B. Letaief, and H. V. Poor, “A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications,” IEEE Commun. Surveys Tuts., pp. 1–1, to appear, 2024.
- J. Ghosh, C. Vargas-Rosales, L. L. Mendes, I.-H. Ra, V. Nhan Vo, P. Aimtongkham, and C. So-In, “A novel transceiver and an asynchronous mode for the hybrid multiple-access hetnet architecture,” IEEE Access, vol. 11, pp. 135 609–135 625, Nov. 2023.
- Z. Wu, M. Cui, Z. Zhang, and L. Dai, “Distance-aware precoding for near-field capacity improvement in XL-MIMO,” in Proc. 2022 IEEE 95th Veh. Technol. Conf. (VTC Spring), Jun. 2022, pp. 1–5.
- A. Singh, V. Petrov, H. Guerboukha, I. V. A. K. Reddy, E. W. Knightly, D. M. Mittleman, and J. M. Jornet, “Wavefront engineering: Realizing efficient terahertz band communications in 6G and beyond,” arXiv:2305.12636, 2023.
- H. Zhang, N. Shlezinger, F. Guidi, D. Dardari, M. F. Imani, and Y. C. Eldar, “Beam focusing for near-field multiuser MIMO communications,” IEEE Trans. Wireless Commun., vol. 21, no. 9, pp. 7476–7490, Sep. 2022.
- J. H. I. de Souza, J. C. M. Filho, A. Amiri, and T. Abrão, “QoS-Aware user scheduling in crowded XL-MIMO systems under non-stationary multi-state LoS/NLoS channels,” IEEE Trans. Veh. Technol., vol. 72, no. 6, pp. 7639–7652, Jun. 2023.
- K. Zhi, C. Pan, H. Ren, K. K. Chai, C.-X. Wang, R. Schober, and X. You, “Performance analysis and low-complexity design for XL-MIMO with near-field spatial non-stationarities,” arXiv:2304.00172, 2023.
- X. Shi, J. Wang, Z. Sun, and J. Song, “Spatial-chirp codebook-based hierarchical beam training for extremely large-scale massive MIMO,” IEEE Trans. Wireless Commun., pp. 1–1, to appear, 2023.
- Z. Wang, X. Mu, and Y. Liu, “Near-field integrated sensing and communications,” IEEE Commun. Lett., vol. 27, no. 8, pp. 2048–2052, May. 2023.
- X. Li, Z. Dong, Y. Zeng, S. Jin, and R. Zhang, “Multi-user modular XL-MIMO communications: Near-field beam focusing pattern and user grouping,” arXiv:2308.11289, 2023.
- J. a. H. I. de Souza, A. Amiri, T. Abrão, E. de Carvalho, and P. Popovski, “Quasi-distributed antenna selection for spectral efficiency maximization in subarray switching XL-MIMO systems,” IEEE Trans. Veh. Technol., vol. 70, no. 7, pp. 6713–6725, Jul. 2021.
- W. Liu, H. Ren, C. Pan, and J. Wang, “Deep learning based beam training for extremely large-scale massive MIMO in near-field domain,” IEEE Commun. Lett., vol. 27, no. 1, pp. 170–174, Jan. 2023.
- Y. Zhang, M. Alrabeiah, and A. Alkhateeb, “Reinforcement learning of beam codebooks in millimeter wave and terahertz MIMO systems,” IEEE Trans. Commun., vol. 70, no. 2, pp. 904–919, Feb. 2022.
- Z. Liu, J. Zhang, Z. Liu, H. Du, Z. Wang, D. Niyato, M. Guizani, and B. Ai, “Cell-free XL-MIMO meets multi-agent reinforcement learning: Architectures, challenges, and future directions,” IEEE Wireless Commun., pp. 1–8, to appear, 2024.
- H. Du, R. Zhang, Y. Liu, J. Wang, Y. Lin, Z. Li, D. Niyato, J. Kang, Z. Xiong, S. Cui, B. Ai, H. Zhou, and D. I. Kim, “Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization,” arXiv:2308.05384, 2023.