Related papers: Distributed Antenna Selection for Massive MIMO usi…
In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution…
This paper introduces a unified deep neural network (DNN)-based precoder for two-user multiple-input multiple-output (MIMO) networks with five objectives: data transmission, energy harvesting, simultaneous wireless information and power…
Reconfigurable meta-surface (RMS) is proposed as a very promising and novel technology, which is composed of a large number of low-cost passive elements, and can achieve passive beamforming by controlling the amplitude and phase of incident…
It is well known that the complex system operation requires the use of new scientific tools and computer simulation. This paper presents a modular approach for modeling and analysis of the complex systems (in communication or transport…
Massive MIMO can greatly increase both spectral and transmit-energy efficiency. This is achieved by allowing the number of antennas and RF chains to grow very large. However, the challenges include high system complexity and hardware energy…
This paper investigates joint antenna selection and optimal transmit power in multi cell massive multiple input multiple output systems. The pilot interference and activated transmit antenna selection plays an essential role in maximizing…
It is well known that the complex system operation requires the use of new scientific tools and computer simulation. This paper presents a modular approach for modeling and analysis of the complex systems (in communication or transport…
Antenna selection (AS) is regarded as the key promising technology to reduce hardware cost but keep relatively high spectral efficiency in multi-antenna systems. By selecting a subset of antennas to transceive messages, AS greatly…
We propose a joint channel estimation and data detection algorithm for massive multilple-input multiple-output systems based on diffusion models. Our proposed method solves the blind inverse problem by sampling from the joint posterior…
We propose a distributed version of the Alternating Direction Method of Multipliers (ADMM) with linear updates for directed networks. We show that if the objective function of the minimization problem is smooth and strongly convex, our…
Reconfigurable antenna multiple-input multiple-output (MIMO) is a promising technology for upcoming 6G communication systems. In this paper, we deal with the problem of configuration selection for reconfigurable antenna MIMO by leveraging…
In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive MIMO system with imperfect CSI and propose a practical two-timescale (TTS) transceiver design to reduce the…
Distributed consensus has been widely studied for sensor network applications. Whereas the asymptotic convergence rate has been extensively explored in prior work, other important and practical issues, including energy efficiency and link…
Compression techniques are essential in distributed optimization and learning algorithms with high-dimensional model parameters, particularly in scenarios with tight communication constraints such as limited bandwidth. This article presents…
A Gibbs distribution based combinatorial optimization algorithm for joint antenna splitting and user scheduling problem in full duplex massive multiple-input multiple-output (MIMO) system is proposed in this paper. The optimal solution of…
This paper proposes a fully distributed termination method for distributed optimization algorithms solved by multiple agents. The proposed method guarantees terminating a distributed optimization algorithm after satisfying the global…
We consider multi-group multicast precoding designs for cell-free massive multiple-input multiple-output (MIMO) systems. To optimize the transmit and receive beamforming strategies, we focus on minimizing the sum of the maximum mean squared…
We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal $x$, and the objective is for the agents to recover $x$ from their collective measurements using only…
Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers,…
Adaptation of a wireless system to the polarization state of the propagation channel can improve reliability and throughput. This paper in particular considers polarization reconfigurable multiple input multiple output (PR-MIMO) systems,…