@article{10.11648/j.wcmc.20261301.12,
  author = {Kusi Ankrah Bonsu and Ebenezer Baidoo Bediako and John Tengviel and Kwame Oteng Gyasi and Emmanuel Ampoma Affum and Kwasi Adu-Boahene Opare},
  title = {Energy-Efficient Joint User Selection and Power Allocation Optimization for MU-MIMO Systems with Limited Feedback},
  journal = {International Journal of Wireless Communications and Mobile Computing},
  volume = {13},
  number = {1},
  pages = {23-44},
  doi = {10.11648/j.wcmc.20261301.12},
  url = {https://doi.org/10.11648/j.wcmc.20261301.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wcmc.20261301.12},
  abstract = {Multi-user multiple-input multiple-output (MU-MIMO) technology is widely regarded as a key enabler for next-generation wireless communication systems (WCS), since it exploits spatial degrees of freedom to increase system throughput without requiring additional bandwidth. However, realizing these gains in practice depends on effective base station (BS)-to-user association and transmit power allocation policies, and on accurate channel state information (CSI) for interference-suppressing precoding. Because uplink feedback bandwidth is limited, base stations in practical MU-MIMO deployments typically operate with only partial CSI, which weakens interference suppression, lowers the signal-to-interference-plus-noise ratio (SINR), and ultimately reduces both the achievable data rate and the energy efficiency (EE) of the system. At the same time, the growing energy demand of wireless networks makes energy-efficient design, rather than throughput alone, an increasingly urgent objective. Motivated by this gap, this paper investigates the joint optimization of user selection and power allocation for maximizing system EE in MU-MIMO systems operating under limited feedback, subject to constraints on maximum transmit power and quality of service (QoS). The resulting optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) model, which is both non-convex and NP-hard, making it intractable to solve directly. To overcome this difficulty, fractional programming is used to recast the problem into an equivalent subtractive form, after which an iterative algorithm is developed that combines a modified Kuhn-Munkres algorithm for optimal user selection with a Lagrangian dual decomposition technique for optimal power allocation. Extensive simulation results show that the proposed algorithm consistently achieves higher system EE and throughput than existing energy-efficient joint user selection and power allocation (JUSPA) schemes under limited feedback, while converging within a small number of iterations and incurring only modest computational overhead. These findings confirm that explicitly accounting for the impact of limited feedback on SINR, rather than assuming perfect CSI, is essential for realizing energy-efficient resource allocation in practical MU-MIMO networks.},
 year = {2026}
}

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TY  - JOUR
T1  - Energy-Efficient Joint User Selection and Power Allocation Optimization for MU-MIMO Systems with Limited Feedback
AU  - Kusi Ankrah Bonsu
AU  - Ebenezer Baidoo Bediako
AU  - John Tengviel
AU  - Kwame Oteng Gyasi
AU  - Emmanuel Ampoma Affum
AU  - Kwasi Adu-Boahene Opare
Y1  - 2026/11/14
PY  - 2026
N1  - https://doi.org/10.11648/j.wcmc.20261301.12
DO  - 10.11648/j.wcmc.20261301.12
T2  - International Journal of Wireless Communications and Mobile Computing
JF  - International Journal of Wireless Communications and Mobile Computing
JO  - International Journal of Wireless Communications and Mobile Computing
SP  - 23
EP  - 44
PB  - Science Publishing Group
SN  - 2330-1015
UR  - https://doi.org/10.11648/j.wcmc.20261301.12
AB  - Multi-user multiple-input multiple-output (MU-MIMO) technology is widely regarded as a key enabler for next-generation wireless communication systems (WCS), since it exploits spatial degrees of freedom to increase system throughput without requiring additional bandwidth. However, realizing these gains in practice depends on effective base station (BS)-to-user association and transmit power allocation policies, and on accurate channel state information (CSI) for interference-suppressing precoding. Because uplink feedback bandwidth is limited, base stations in practical MU-MIMO deployments typically operate with only partial CSI, which weakens interference suppression, lowers the signal-to-interference-plus-noise ratio (SINR), and ultimately reduces both the achievable data rate and the energy efficiency (EE) of the system. At the same time, the growing energy demand of wireless networks makes energy-efficient design, rather than throughput alone, an increasingly urgent objective. Motivated by this gap, this paper investigates the joint optimization of user selection and power allocation for maximizing system EE in MU-MIMO systems operating under limited feedback, subject to constraints on maximum transmit power and quality of service (QoS). The resulting optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) model, which is both non-convex and NP-hard, making it intractable to solve directly. To overcome this difficulty, fractional programming is used to recast the problem into an equivalent subtractive form, after which an iterative algorithm is developed that combines a modified Kuhn-Munkres algorithm for optimal user selection with a Lagrangian dual decomposition technique for optimal power allocation. Extensive simulation results show that the proposed algorithm consistently achieves higher system EE and throughput than existing energy-efficient joint user selection and power allocation (JUSPA) schemes under limited feedback, while converging within a small number of iterations and incurring only modest computational overhead. These findings confirm that explicitly accounting for the impact of limited feedback on SINR, rather than assuming perfect CSI, is essential for realizing energy-efficient resource allocation in practical MU-MIMO networks.
VL  - 13
IS  - 1
ER  - 

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