Beamforming is an effective means to improve the quality of received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and thus not suitable for real-time implementation. In this paper, we propose a deep learning framework optimization downlink beamforming. particular, obtained based convolutional neural networks exploitation expert knowledge, such as...

Beamforming is considered as one of the most important techniques for designing advanced multiple-input and multiple-output (MIMO) systems. Among existing design criterions, sum rate maximization (SRM) under a total power constraint challenge due to its nonconvexity. Existing SRM problem only obtain local optimal solutions but require huge amount computation their complex matrix operations iterations. Unlike these conventional methods, we propose deep learning based fast beamforming method...

In the downlink transmission scenario, power allocation and beamforming design at transmitter are essential when using multiple antenna arrays. This paper considers a input–multiple output broadcast channel to maximize weighted sum-rate under total constraint. The classical minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose fast method unsupervised learning, which trains deep neural...

By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected one server for task processing, computation can strike a balance between MD's execution delay and energy consumption in edge computing (MEC) systems. Considering communication dynamics MEC systems, we aim design online mechanisms this paper minimize time average expected under constraint consumption. Firstly, with known current channel gains MD APs as well available...

This paper investigates the uplink performance of a general cell-free massive multiple-input multiple-output (CF-mMIMO) system, in which all access points (APs) and user equipments (UEs) suffer from hardware impairments (HWIs). Besides, there are several reconfigurable intelligent surfaces (RISs) that aim to improve coverage quality, spectral efficiency (SE), energy (EE). Relying on knowledge only imperfect channel state information, tight closed-form expression for lower-bound achievable SE...

Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted MIMO downlink system suffering from transceiver hardware impairments (T-HWIs) investigated. To improve accuracy of direct estimation (DE) scheme, modified ON/OFF (MOE) with moderate pilot overhead proposed. Relying on knowledge imperfect channel...

In the realm of industrial Internet Things, imperative for ultra-reliable and low-latency communication (URLLC) is underscored by demand up to 99.999% reliability sub-microsecond latency. this paper, we delve into a downlink cell-free massive multiple-input multiple-output (MIMO) system designed facilitate URLLC, operating over spatially correlated Rician fading channels with inherent phase shifts. Utilizing short-packet transmission accounting imperfect channel state information, derive...

Federated learning (FL) enables clients to collaboratively learn a shared task while keeping data privacy, which can be adopted at the edge of wireless networks improve intelligence. In this letter, we aim minimize training latency FL system for given loss by client scheduling. Instead assuming that prior information about channel state and local computing power is available, consider more practical scenario without knowing information. We first reformulate scheduling problem as multi-armed...

Motivated by the ever-increasing demands for massive data processing and intelligent analysis at network edge, federated learning (FL), a distributed architecture machine learning, has been introduced to enhance edge intelligence without compromising privacy. Nonetheless, due large number of devices (referred as clients in FL) with only limited wireless resources, client scheduling, which chooses subset participate each round FL, becomes more feasible option. Unfortunately, training latency...

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions achievable secrecy rate relied on additive quantization noise model derived. Based these analytical results, we quantify...

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed as an enhancement model. Such a system generally consists three entities, i.e., the server, helpers, and clients, in which each helper collects trained gradients from clients nearby, aggregates them, sends result to server for global model update. Due limited communication resources, only portion helpers can be scheduled upload their aggregated round training. And that necessitates well-designed scheme...

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -nearest neighbors (KNN) algorithms are widely used for indoor fingerprint positioning, but conventional KNN usually adopt received signal strength (RSS) similarity as a metric to select reference points (RPs) position determination, which may lead inaccurate positioning results. This is because RSS cannot well reflect...

This paper proposes a joint design of transmit beam-forming and reconfigurable intelligent surface (RIS) reflecting for radar sensing single-user communications. The receiving signal-to-noise ratio (SNR) is employed as performance metric target estimation which we aim to maximize while guaranteeing pre-defined SNR the user communication. obtained problem non-convex, thus resort alternating maximization well successive lower bound methods pursuit stationary solution. Simulation results...

Traffic delay is a key metric to measure the quality-of-service of next-generation wireless communication networks. In this paper, we consider cloud radio access network architecture with hierarchical structure virtual controllers and multiple clusters remote heads (RRHs). A high-level controller coordinates control plane decisions among local each in charge cluster RRHs. Moreover, equipped one server for creating machines (VMs) execute users' baseband processing tasks. Then, under...

Federated learning (FL) is promising in enabling large-scale model training by massive devices without exposing their local datasets. However, due to limited wireless resources, traditional cloud-based FL system suffers from the bottleneck of communication overhead core network. Fueled this issue, we consider a hierarchical and formulate joint problem edge aggregation interval control resource allocation minimize weighted sum loss latency. To quantify performance, an upper bound average...

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet Things (IoT) networks relying on cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. such a network, access points (APs) beam radio-frequency (RF) energy toward IoT sensors during DL phase. Tight closed-form expressions for harvested (HE) achievable rate with conjugate beamforming (CB) normalized CB (NCB) are, respectively, derived,...

This paper studies a simultaneously transmitting and reflecting reconfigurable intelligent surface aided non-orthogonal multiple access system with two paired users. To ensure the quality of service requirement reflected user, we propose joint power discrete amplitude allocation scheme, which can reduce channel estimation workload hardware complexity. characterize performance our proposed novel statistic over Nagakami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML"...

This letter investigates the physical layer security of reconfigurable intelligent surface (RIS) aided integrated-sensing-and-communication (ISAC) systems, in which high-power radar signal is used as interference to disturb eavesdropper. We formulate a joint beamforming optimization problem active base station and passive RIS minimize maximum eavesdropping signal-to-interference-plus-noise (SINR). To find its solution, we decompose original into two subproblems then use alternating (AO)...