The increasing complexity of real-time healthcare necessitates intelligent systems for dynamic data management and personalized assistance. This paper proposes a novel dual-LLM framework that integrates large language models (LLMs) into wireless healthcare networks. The first LLM powers an interactive artificial intelligence module (IAIM) embedded within a mobile edge computing (MEC) environment, which dynamically optimizes user-specific data routing and reconfigurable intelligent surface (RIS) configurations via a modified proximal policy optimization (PPO) algorithm. A novel Greedy Look-Ahead Algorithm (GLAA) is introduced for real-time path selection based on signal strength, emergency factors, and user-specific parameters. The second LLM, utilizing a retrieval-augmented generation (RAG) approach, serves as a personalized healthcare chat assistant that delivers context-aware patient support using real-time and historical data. Simulation results demonstrate that the proposed IAIM achieves a 9.6% reduction in network overhead compared to manual modeling and reduces latency by up to 52.5% over baseline PPO approaches, thus enabling enhanced user experience and responsiveness in healthcare systems.

One of the disruptive communication technologies for sixth-generation (6G) wireless networks is cell-free massive multiple-input multiple-output (CF-mMIMO), which is capable to control inter-cell interference in MIMO systems. This paper investigates the performance of a full-duplex (FD) CF-mMIMO systems with practical limited-capacity fronthaul links. The proposed system employs a large number of M distributed FD APs, arbitrarily distributed$K_{d}$downlink (DL) and$K_{u}$uplink (UL) half-duplex (HD) single-antenna equipped user terminals (UEs), and a central processing unit (CPU). To exploit the energy efficiency and potential throughput gains of FD systems, each AP is linked to the CPU through a fronthaul link with limited capacity that handles the quantized UL/DL data to/from the CPU. Each AP is expected to support K HD UEs on the same spectrum resource, where$K = (K_{u} + K_{d})$. Imperfect channel state information and the mobility of the UEs are also considered. A closed-form expression for the outage probability is derived using the optimal uniform quantization and maximum-ratio combining/maximum-ratio transmission considering the Welch-Satterthwaite approximation. Additionally, the asymptotic and infinite-M outage expressions for the proposed system are analytically studied and verified via Monte Carlo simulation. Simulation results demonstrate the relationship between the improved outage performance and uniform quality of service (QoS) for all UEs. Moreover, this analysis provides valuable insights into the behavior of FD-CF-mMIMO system and underscores the importance of providing a uniform QoS to all UEs in improving the overall performance of the system.

In the context of ultra-reliable low-latency communication (URLLC) in Internet-of-Things (IoT) networks, conventional half-space coverage limits the flexibility of reconfigurable intelligent surface (RIS) deployment. To overcome these constraints, this paper makes use of active simultaneously transmitting and reflecting RIS (STAR-RIS), which is seamlessly integrated into digital twin (DT) and mobile edge computing (MEC) frameworks. Our primary research objective is to achieve full-space coverage by enabling simultaneous transmission and reflection of the signals while improving uplink data transmission from IoT URLLC user nodes (UNs) to the base station (BS) with the assistance of active STAR-RIS, even in the presence of imperfect channel state information (CSI). We formulate the problem of minimizing total end-to-end (e2e) latency, computed using the alternating optimization (AO) algorithm. Subsequently, we have evaluated the performance of the AO algorithm against the stochastic gradient descent (SGD) algorithm, which serves as the benchmark solution. The simulation outcomes delineate a performance evaluation under perfect and imperfect CSI scenarios. The AO algorithm outperforms SGD with latency reductions of 19.7% at$N=32$and 20.4% at$N=64$. Increasing N from 32 to 64 results in a 39.3% latency reduction for AO, surpassing SGD’s 38.8%. However, the SGD algorithm consistently exhibits lower computational complexity compared to the AO algorithm. Additionally, the energy splitting mode achieves the system’s total e2e latency reductions of 28.4% over the mode switching mode and 11.04% over time switching mode. Furthermore, active STAR-RIS optimal beamforming (ARO) achieves$\approx 10$% latency reduction over the predictive optimal beamforming (PRO), which itself surpasses active STAR-RIS with random beamforming (ARR) by$\approx 9$%. This comparison considers key factors such as the power budget, the number of RIS elements, the caching capacity of the edge computing server (ECS), the number of IoT UNs, the minimum transmission rate, and maximum transmit power at BS of active STAR-RIS.

In the ever-evolving landscape of next-generation wireless communication systems, the need for high data rates, seamless connectivity, and energy efficiency continues to increase. To meet these demands, the integration of emerging technologies such as cell-free massive multiple-input multiple-output (CFm-MIMO) and reconfigurable intelligent surfaces (RIS) has gained significant attention. This paper presents a comprehensive performance analysis of a downlink active RIS-assisted CFm-MIMO system in the context of user terminal (UT) mobility, emphasizing the critical aspect of constrained fronthaul capacity. The paper employs a rigorous analytical framework to determine the outage performance of the proposed system considering imperfections in the channel state information (CSI). The findings presented in this paper demonstrate the impact of the number of RIS elements ($N$), quantization parameters, UT mobility, scattering models, and imperfect CSI on the outage probability. It is revealed that by increasing the$N$from 8 to 32 will significantly decreases the OP by 99.75%. Moreover, it is interesting to observe that the choice and optimization of quantization parameters remain consistent regardless of RIS presence.

This paper explores a cutting-edge terahertz (THz) integrated sensing and communication system (ISAC) that utilizes active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (A-STAR-RIS). The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicles, and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system features a dual-function radar and communication multiple antenna base station (BS), serving both communication and target sensing functions concurrently through an A-STAR-RIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, A-STAR-RIS reflection and transmission beamforming matrices, vehicular unit (VU) mobility correlation parameters, and radar receive filter. Given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables, traditional optimization methods prove challenging. To overcome this, we propose a machine learning (ML) based deep deterministic policy gradient (DDPG) algorithm. Our simulations validate the substantial benefits of A-STAR-RIS over conventional benchmark scenarios.

This paper presents a novel design for a mobile edge computing (MEC) service that integrates digital twin technology with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). This configuration leverages edge intelligence, aiming to strengthen ultra-reliable and low-latency communications (URLLC) within Internet-of-Things (IoT) frameworks. We explore the uplink data transmission path from singular-antenna IoT URLLC nodes (UNs) to a multi-antenna base station (BS) facilitated by an active STAR-RIS. Our focus is on framing an end-to-end (e2e) latency reduction strategy for the presented system. Due to the inherent non-convexity of this problem, we propose an efficient alternating optimization (AO) algorithm to get a solution. This algorithm decomposes the main problem into five distinct sub-problems: transmit beamforming design, optimization of caching and offloading policies, joint communication and computation optimization, and enhancement of active STAR-RIS beamforming. An extensive set of simulation outcomes indicates that our DT-enhanced optimal-phase STARRIS approach consistently surpasses benchmark methods, particularly when accounting for variables such as power constraints, the number of RIS elements, the caching capacity of the edge computing server (ECS), and the number of IoT UNs.

This paper investigates a digital twin (DT) and reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (E2E) latency minimization for the considered system under the joint optimization of beamforming design at the RIS, power, bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP). Later, we adopt deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm to solve it effectively. We have compared the DDPG results with proximal policy optimization (PPO), modified PPO (M-PPO), and conventional alternating optimization (AO) algorithms. Simulation results depict that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network using DDPG algorithm achieves up to 60% lower transmission delay and 20% lower energy consumption compared to the scheme without an RIS. This confirms the practical advantages of leveraging RIS technology in MEC systems. Results demonstrate that DDPG outperforms M-PPO and PPO in terms of higher reward value and better learning efficiency, while M-PPO and PPO exhibit lower execution time than DDPG and AO due to their advanced policy optimization techniques. Thus, the results validate the effectiveness of the DRL solutions over AO for dynamic resource allocation w.r.t. reduced execution time.

Re-configurable intelligent surface (RIS)-aided communication has been envisaged as a frontier scheme to enable ultra-high spectral efficiency (SE) and energy efficiency (EE) for next-generation communication. This paper investigates an unconventional framework of RIS-aided full-duplex (FD) multi-user multiple-input multiple-output (MIMO) communication and analyzes its resource efficiency (RE), a preferable performance metric for realizing trade-off between SE and EE maximization. In particular, we focus on the RE maximization problem via a joint optimization of transmit covariance, optimal receive covariance, and phase-shift matrices for each RIS subject to the given constraint on the power budget. To solve the formulated non-convex problem, we propose two optimization approaches: a) policy gradient-based deep-reinforcement learning (DRL) algorithm based on a Markov decision process formulation for a stochastic-time varying channel and b) alternate optimization (AO) algorithm based on general approximations and majorization-minimization (MM) for static channel conditions. Simulation results validate the out-performance of the considered RIS-aided FD-MIMO system compared to the counterpart system with half-duplex (HD) mode and without RIS case. The proposed DRL algorithm achieves comparable RE performance with reduced computational complexity and running time compared to the traditional AO-based algorithm.

The substantial power consumption attributed to the active components within fully-connected reconfigurable intelligent surface (RIS) architecture significantly hinders the efficiency and sustainability of DT-enabled MEC networks. To tackle this challenge, we present an innovative sub-connected architecture for active RIS within the digital twin (DT) integrated mobile edge computing (MEC) framework of an Internet-of-Things (IoT) networks, capitalizing on edge intelligence to enhance ultra-reliable and low-latency communication (URLLC) services. The primary aim of our research is to improve uplink data transmission from IoT URLLC user nodes (UNs) to a base station (BS) with the aid of an active RIS, even under an imperfect channel state information (CSI). We have formulated the total end-to-end (e2e) latency minimization problem, which is solved by using an efficient alternating optimization (AO) algorithm. The algorithm breaks down the proposed non-convex problem into five subproblems, namely, beamforming design, caching and offloading policy optimization, joint communication and computation optimization, and joint active RIS phase shift and amplification factor vector optimization. We conducted a thorough analysis of the convergence properties of the proposed AO algorithm, benchmarking its performance against the established Heuristic algorithm. Our simulation results consistently demonstrate the superiority of our proposed DT-assisted optimal phase sub-connected active RIS scheme over various benchmark schemes, taking into account various factors such as the number of RIS elements, power budget constraints, imperfect CSI, edge computing server (ECS) cache capacity, number of IoT UNs, and the number of power amplifiers.

This paper explores the state-of-the-art terahertz (THz) integrated sensing and communication system (ISAC) that uses active reconfigurable intelligent surfaces (ASRIS) that can transmit and reflect signals at the same time. The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicular units (VUs), and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system is equipped with a dual-function radar and communication multiple-antenna base station (BS), which simultaneously serves both communication and target sensing functions through an ASRIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, ASRIS reflection and transmission beamforming matrices, VU mobility correlation parameters, and radar receive filter. Traditional optimization methods prove challenging given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables. To overcome this, we propose a machine learning (ML)-based multi-agent deep deterministic policy gradient (MADDPG) algorithm. MADDPG enables collaborative learning, adapts to the dynamic communication environment, and excels in optimizing interdependent parameters in the proposed THz system. Deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and modified-PPO (MPPO) algorithms serve as benchmarks, showcasing the distinctive advantages of the ML-based MADDPG solution for the proposed system’s complexities. Our simulations validate the substantial benefits of ASRIS over conventional RIS benchmark scenarios.

This work proposes a novel design for an active reconfigurable intelligent surface (RIS)-assisted digital twin (DT) based mobile edge computing (MEC) model that leverages edge intelligence to enhance ultra-reliable and low-latency communications (URLLC) services in Internet-of- Things (loT) networks. The system model considers uplink data transmission from the single antenna IoT-URLLC nodes (UNs) to a multi-antenna base station (BS) with the aid of an active RIS under imperfect channel state information (CSI). We formulate a total end-to-end (E2E) latency minimization problem for the proposed system model. An efficient alternating optimization (AO) algorithm is proposed to tackle the non-convexity of the problem by reformulating it into five subproblems: beamforming design, caching and offloading policies optimization, joint communication and computation optimization, and active RIS phase shift optimization. Simulation results demonstrate that the proposed DT-assisted optimal-phase active RIS scheme consistently outperforms benchmark schemes, such as optimal-phase passive RIS, random-phase active RIS, and no- RIS systems, considering factors such as imperfect CSI, power budget, number of RIS elements, the caching capacity of edge computing server (ECS) and the number of loT UNs.

The cell-free massive multiple-input and multiple-output (CF-mMIMO) communication technology has the ability to handle inter-cell interference in MIMO systems, making it a potential candidate for sixth-generation (6G) wireless communication. A CF-mMIMO system is investigated in this paper for mission-critical ultra-reliable low latency communication (URLLC) applications involving a central processing unit (CPU), many distributed access points (APs), each with multiple antennas, and multiple single-antenna user equipment (UEs). In order to maximize energy efficiency (EE) and throughput gains, each AP is linked to the CPU through a fronthaul link with limited capacity, which handles the quantized uplink data to the CPU. We assume that each AP serves fewer UEs. Our approach has a minimal signal processing complexity and offers UEs uniform quality of service (QoS) as well as improved EE. Closed-form expression for outage probability (OP) in the uplink of the CF-mMIMO system considering Welch-Satterthwaite approximation is derived using a variety of Doppler power spectra (DPS) models that consider imperfect channel state information (CSI) and mobility of UEs. Our numerical simulations validate the correctness of the derived expressions.

Reconfigurable intelligent surface (RIS) is a break-through technology that enhances both energy efficiency (EE) and spectrum efficiency (SE) by artificial reconfiguration of the electromagnetic waves utilizing the reflective property of the metasurface elements. This work studies the optimization of the SE-EE trade-off using the deep reinforcement learning (DRL) algorithm in a RIS-assisted full-duplex multi-user multiple-input multiple-output (MIMO) communication system. We use partial channel state information to control the overhead signaling requirement and demand for energy supply to the system. We consider resource efficiency (RE), in which the RIS’s phase-shift design and power allocation at the nodes (i.e., node in BS in downlink (DL) and user in uplink (UL)) are jointly optimized, with the goal of investigating the SE-EE trade-off of the considered system using an appropriate performance metric. We adopt a DRL-based approach for the proposed system to tackle the challenges involved in optimization due to time-varying channels and exploitation in real-time applications. Additionally, simulation outcomes exemplify the efficiency and swift conver-gence rate of the proposed algorithm and demonstrate how different system characteristics, including co-channel interference (CCI), residual self-interference (RSI), and the number of RIS reflecting elements, affect the system’s performance.

The efficient and effective framework for next-generation (5G and beyond 5G) wireless networks should include mission-critical aspects such as ultralow latency ($\leq \!\!1$ms), ultrahigh reliability (99.999%), and enhanced data rate. Billions of ubiquitously connected devices are expected to serve various industrial applications in upcoming industry standards such as Industry 5.0. These industrial applications include mission-critical tasks such as smart grids, remote surgery, and intelligent transportation systems. This article considers an industrial Internet of Things (IIoT) environment in mission-critical ultrareliable low latency communication (URLLC) application where the main industrial unit or industrial control node (CN) sends messages to the target device (TD) with the aid of a cooperative device (CD). We investigate a novel transmission protocol and analyze the network’s performance. Considering the nonlinear energy harvesting (EH) mechanism at power-constrained nodes and direct and cooperative phase transmissions, the outage probability (OP) and block error rate (BLER) performances are evaluated for Rayleigh distributed fading channels. The analytical results are validated through Monte–Carlo simulations.

We consider a full-duplex (FD)-enabled multi-user two-way communication system with adaptive amplify-and-forward (AF)/decode-and-forward (DF) relaying protocol. All the users are assumed to be mobile and to have FD abilities. The effect of mobility, which results in time-selective fading, is modeled using a first-order autoregressive (AR1) process. The fading channel-based approach characterizes the residual self-interference (RSI) at the FD relay, user nodes and is modeled as a Rician distributed random variable. This paper constitutes the most critical use case of 5G, i.e., ultra-reliable low latency communication (URLLC), which adopts short-packets finite blocklength (FB) codes to spin out into the mission-critical applications where strict latency and reliability requirements are highly desirable. The outage performance of the system is studied over independent and non-identically distributed complex Gaussian (Rayleigh envelope) channels with imperfect channel state information (CSI) for with and without URLLC use cases. The closed-form expressions for the outage probability and block error rate (BLER) are derived for the absolute channel power-based scheduling scheme considering the different Doppler power spectra models and the effect of co-channel interference (CCI). The expressions for the asymptotic outage probability are also derived. The presented analysis is compared with baseline schemes, e.g., the results derived with adaptive AF/DF relaying are also compared with both AF and DF relaying, and the performance of the FD transmissions is compared to that of half-duplex (HD) transmissions. The impact of node mobility, RSI, FBL, number of user pairs, and imperfect CSI on the system performance is investigated. Moreover, essential insights are obtained related to the performance gain and region of the superiority of the adaptive AF/DF relaying scheme. The derived analytic results are validated through Monte Carlo simulations. Furthermore, at high transmit power, the outage performance for the adaptive AF/DF protocol approaches the derived asymptotic floor.

We consider an Internet of things (IoT) environment where the base station (BS) sends messages to the target user (TU) with the help of a cooperative user (CU). The cooperative user is selected based on the instantaneous signal-to-noise ratio (SNR) of the BS- TU link. For the considered cooperative IoT model, we further propose a new transmission protocol that incorporates the performance of active users and non-linear energy harvesting (EH) into account. The EH is assumed at every node, and the performance of the considered framework is evaluated in terms of outage probability. Specifically, for Rayleigh distributed fading channels, the closed-form expression for the outage probability is derived. The analytical results are validated through Monte-Carlo simulations. We also illustrated the impact of the number of CU and other parameters on the system's performance.