References & Further Reading

References

  1. E. Bjornson, J. Hoydis, and L. Sanguinetti, Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency, 2017

    The definitive reference for power control in massive MIMO. Chapter 7 covers max-min, proportional fairness, and sum-rate maximization with full proofs.

  2. R. D. Yates, A Framework for Uplink Power Control in Cellular Radio Systems, 1995

    Foundational paper establishing the standard interference function framework. Every iterative power control algorithm traces back to this work.

  3. F. P. Kelly, Charging and Rate Control for Elastic Traffic, 1998

    Introduces proportional fairness in network resource allocation. The connection to Nash bargaining and the PF scheduler derives from this paper.

  4. J. F. Nash, The Bargaining Problem, 1950

    Nash's original paper on the bargaining problem. The product-of-utilities solution axiomatized here is the foundation of proportional fairness.

  5. Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel, 2011

    The WMMSE algorithm paper. Establishes the rate-MSE duality and the alternating optimization framework for sum-rate maximization.

  6. Z.-Q. Luo and S. Zhang, Dynamic Spectrum Management: Complexity and Duality, 2008

    Proves NP-hardness of the sum-rate maximization problem for interference channels via reduction from MAX-CUT.

  7. M. Chiang, C. W. Tan, D. P. Palomar, D. O'Neill, and D. Julian, Power Control By Geometric Programming, 2005

    Shows how wireless power control problems can be cast as geometric programs. The GP formulation enables efficient global optimization.

  8. J. Mo and J. Walrand, Fair End-to-End Window-Based Congestion Control, 2000

    Introduces the alpha-fairness utility family that unifies sum-rate, proportional fairness, and max-min fairness.

  9. B. R. Marks and G. P. Wright, A General Inner Approximation Algorithm for Nonconvex Mathematical Programs, 1978

    The original paper on successive convex approximation (SCA). Establishes the convergence theory used in Section 5.3.

  10. D. Tse and P. Viswanath, Fundamentals of Wireless Communication, Cambridge University Press, 2005

    Chapter 6 covers opportunistic communication and the PF scheduler. The multiuser diversity principle connects proportional fairness to fading channel capacity.

  11. 3GPP, NR; Physical layer procedures for control, 2023. [Link]

    Section 7.1 defines the uplink power control formula used in 5G NR, including the fractional power control parameters.

  12. T. L. Marzetta, E. G. Larsson, H. Yang, and H. Q. Ngo, Fundamentals of Massive MIMO, Cambridge University Press, 2016

    Chapter 5 covers power control for massive MIMO. Provides an accessible treatment complementary to the BHS monograph.

  13. H. Q. Ngo, E. G. Larsson, and T. L. Marzetta, Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems, 2013

    Analyzes spectral and energy efficiency with power control in massive MIMO. Derives the power scaling law that transmit power can decrease as $1/M$ with no rate loss.

Further Reading

For readers who want to go deeper into specific topics from this chapter.

  • Convex optimization theory

    S. Boyd and L. Vandenberghe, *Convex Optimization*, Cambridge University Press, 2004

    Provides the optimization foundations (KKT, duality, interior point methods) used throughout this chapter. Freely available online.

  • Geometric programming for wireless

    M. Chiang, *Geometric Programming for Communication Systems*, Now Publishers, 2005

    A tutorial-length treatment of GP formulations in wireless. Covers the high-SINR approximation and GP-based power control in depth.

  • Game theory for resource allocation

    Z. Han, D. Niyato, W. Saad, T. Basar, and A. Hjorungnes, *Game Theory in Wireless and Communication Networks*, Cambridge University Press, 2012

    Extends the Nash bargaining framework to non-cooperative and coalitional games, covering topics like pricing-based power control and distributed spectrum sharing.

  • Deep learning for power control

    H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, 'Learning to Optimize: Training Deep Neural Networks for Interference Management,' IEEE TSP, 2018

    Shows how neural networks can approximate the WMMSE algorithm at a fraction of the computational cost. Connects to Chapter 25 (AI/ML for Massive MIMO).

  • Cell-free massive MIMO power control

    H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson, and T. L. Marzetta, 'Cell-Free Massive MIMO Versus Small Cells,' IEEE TWC, 2017

    Extends the max-min power control framework to cell-free distributed MIMO. Essential reading for Part III of this book.

ExercisesCh 6 →