Corpus ID: 237513724

ML-aided power allocation for Tactical MIMO

  title={ML-aided power allocation for Tactical MIMO},
  author={Arindam Chowdhury and Gunjan Verma and Chirag R. Rao and Ananthram Swami and Santiago Segarra},
We study the problem of optimal power allocation in single-hop multi-antenna ad-hoc wireless networks. A standard technique to solve this problem involves optimizing a tri-convex function under power constraints using a blockcoordinate-descent (BCD) based iterative algorithm. This approach, termed WMMSE, tends to be computationally complex and time consuming. Several learning-based approaches have been proposed to speed up the power allocation process. A recent work, UWMMSE, learns an affine… Expand

Figures from this paper

Robust MIMO Detection using Hypernetworks with Learned Regularizers
This work proposes a method that tries to strike a balance between symbol error rate (SER) performance and generality of channels, based on hypernetworks that generate the parameters of a neural network-based detector that works well on a specific channel. Expand
Stability Analysis of Unfolded WMMSE for Power Allocation
This paper focuses on UWMMSE – a modern algorithm leveraging graph neural networks –, and illustrates its stability to additive input perturbations of bounded energy through both theoretical analysis and empirical validation. Expand
Power Allocation for Wireless Federated Learning using Graph Neural Networks
This work proposes a data-driven approach for power allocation in the context of federated learning (FL) over interference-limited wireless networks that outperforms three baseline methods in both transmission success rate and FL global performance. Expand


An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel
This paper proposes a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean squared error (MSE) and extends the algorithm to a general class of utility functions and establishes its convergence. Expand
Deep unfolding of the weighted MMSE beamforming algorithm
This work proposes the novel application of deep unfolding to the WMMSE algorithm for a MISO downlink channel, and presents an alternative formulation that circumvents these operations by resorting to projected gradient descent. Expand
Dynamic Spectrum Management: Complexity and Duality
Using the Lyapunov theorem in functional analysis, this work rigorously proves a result first discovered by Yu and Lui (2006) that there is a zero duality gap for the continuous (Lebesgue integral) formulation of the discretized version of this nonconvex problem. Expand
Learning Optimal Resource Allocations in Wireless Systems
DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parameterization of the resource allocation policy and optimizes the primal and dual variables. Expand
Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems
This work proposes a framework for deep- unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Expand
Massive MIMO for Tactical Ad-hoc Networks in RF Contested Environments
This paper presents a novel massive MIMO communications system which enhances the throughput of the network, reduces the bit-error-rate and mitigates the interference from high powered jammers. Expand
Towards Optimal Power Control via Ensembling Deep Neural Networks
Simulation results show that for the standard symmetric $K$ -user Gaussian interference channel, the proposed methods can outperform state-of-the-art power control solutions under a variety of system configurations. Expand
Adaptive Contention Window Design Using Deep Q-Learning
A rainbow agent is implemented, which incorporates several empirical improvements over the basic deep Q-network and performs close to optimal and markedly improves upon existing learning and non-learning based alternatives. Expand
Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays
It is explained how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun, how the coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. Expand
Signal Processing and Optimal Resource Allocation for the Interference Channel
The computational complexity, the convexity as well as the duality of the optimal resource allocation problem is discussed, and various existing algorithms for resource allocation are presented and compared. Expand