Intelligent Reflecting Surfaces and Next Generation Wireless Systems

Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. For the slot-by-slot reflection optimization, we propose exploiting an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget. Then, we analyze the impact of the distributed IRS on the channel rank. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, we present a two-timescale reflection optimization scheme, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples.

Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round. This paper presents a new multi-carrier non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive learning setting of Flexible Aggregation. Since a WFL round accommodates both local model training and uploading for each user, the use of Flexible Aggregation allows the users to train different numbers of iterations per round, adapting to their channel conditions and computing resources. The key idea is to use MC-NOMA to concurrently upload the local models of the users, thereby extending the local model training times of the users and increasing participating users. A new metric, namely, Weighted Global Proportion of Trained Mini-batches (WGPTM), is analytically established to measure the convergence of the new system. Another important aspect is that we maximize the WGPTM to harness the convergence of the new system by jointly optimizing the transmit powers and subchannel bandwidths. This nonconvex problem is converted equivalently to a tractable convex problem and solved efficiently using variable substitution and Cauchy's inequality. As corroborated experimentally using a convolutional neural network and an 18-layer residential network, the proposed MC-NOMA WFL can efficiently reduce communication delay, increase local model training times, and accelerate the convergence by over 40%, compared to its existing alternative.

Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems

This paper puts forth a new, reconfigurable intelligent surface (RIS)-assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT). Specifically, we propose a novel learning framework that maximizes the sum-rate by jointly optimizing the access point and user association (AUA), power control, and RIS beamforming. This problem is challenging and has never been addressed due to its prohibitively large and complex solution space. Our framework decouples the AUA from the power control and RIS beamforming (PCRB) based on the different natures of their variables, hence reducing the solution space. A new position-adaptive binary particle swarm optimization (PABPSO) method is designed for the AUA. Two twin-delayed deep deterministic policy gradient (TD3) models with new and refined state pre-processing layers are developed for the PCRB. Another important aspect is that a DT is leveraged to train the learning framework with its replay of channel estimates stored. The AUA, power control, and RIS beamforming are only tested in the physical environment at the end of selected epochs. Simulations show that using RISs contributes to considerable increases in the sum-rate of UCCF systems, and the DT dramatically reduces overhead with marginal performance loss. The proposed framework is superior to its alternatives in terms of sum-rate and convergence stability.

On Secure Uplink Transmission in Hybrid RF-FSO Cooperative Satellite-Aerial-Terrestrial Networks

This work investigates the secrecy outage performance of the uplink transmission of a radio-frequency (RF)-free-space optical (FSO) hybrid cooperative satellite-aerial-terrestrial network (SATN). Specifically, in the considered cooperative SATN, a terrestrial source (S) transmits its information to a satellite receiver (D) via the help of a cache-enabled aerial relay (R) terminal with the most popular content caching scheme, while a group of eavesdropping aerial terminals (Eves) trying to overhear the transmitted confidential information. Moreover, RF and FSO transmissions are employed over S-R and R-D links, respectively. Considering the randomness of R, D, and Eves, and employing a stochastic geometry framework, the secrecy outage performance of the cooperative uplink transmission in the considered SATN is investigated and a closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, Monte-Carlo simulations are shown to verify the accuracy of our analysis.

Multi-Agent Deep Reinforcement Learning for Cost- and Delay-Sensitive Virtual Network Function Placement and Routing

This paper proposes an effective and novel multiagent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same time. The differentiated demands of the service requests are reflected by their delay- and cost-sensitive factors. We first construct a VNF P&R problem to jointly minimize a weighted sum of service delay and resource consumption cost, which is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask. Each subtask consists of multiple concurrent parallel sequential decision processes. By invoking the deep deterministic policy gradient method and multi-agent technique, an MADRL-P&R framework is designed to perform the two subtasks. The new joint reward and internal rewards mechanism is proposed to match the goals and constraints of the placement and routing subtasks. We also propose the parameter migration-based model-retraining method to deal with changing network topologies. Corroborated by experiments, the proposed MADRL-P&R framework is superior to its alternatives in terms of service cost and delay, and offers higher flexibility for personalized service demands. The parameter migration-based model-retraining method can efficiently accelerate convergence under moderate network topology changes.

Two-Timescale Optimization for Intelligent Reflecting Surface-Assisted MIMO Transmission in Fast-Changing Channels

The application of intelligent reflecting surface (IRS) depends on the knowledge of channel state information (CSI), and has been hindered by the heavy overhead of channel training, estimation, and feedback in fast-changing channels. This paper presents a new two-timescale beamforming approach to maximizing the average achievable rate (AAR) of IRS-assisted MIMO systems, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the base station precoder and power allocation are updated frequently based on quickly outdated instantaneous CSI (I-CSI). The key idea is that we first reveal the optimal small-timescale power allocation based on outdated I-CSI yields a water-filling structure. Given the optimal power allocation, a new mini-batch sampling (mbs)- based particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS configuration with reduced channel samples. Another important aspect is that we develop a model-driven PSO algorithm to optimize the IRS configuration, which maximizes a lower bound of the AAR by only using the S-CSI and eliminates the need of channel samples. The modeldriven PSO serves as a dependable lower bound for the mbs-PSO. Simulations corroborate the superiority of the new two-timescale beamforming strategy to its alternatives in terms of the AAR and efficiency, with the benefits of the IRS demonstrated.

Effect of Strong Time-Varying Transmission Distance on LEO Satellite-Terrestrial Deliveries

In this paper, we investigate the effect of the strong time-varying transmission distance on the performance of the low-earth orbit (LEO) satellite-terrestrial transmission (STT) system. We propose a new analytical framework using finite-state Markov channel (FSMC) model and time discretization method. Moreover, to demonstrate the applications of the proposed framework, the performances of two adaptive transmissions, rate-adaptive transmission (RAT) and power-adaptive transmission (PAT) schemes, are evaluated for the cases when the transmit power or the transmission rate at the LEO satellite is fixed. Closed-form expressions for the throughput, energy efficiency (EE), and delay outage rate (DOR) of the considered systems are derived and verified, which are capable of addressing the capacity, energy efficiency, and outage rate performance of the considered LEO STT scenarios with the proposed analytical framework.

Downlink Power Minimization in Intelligent Reconfigurable Surface-Aided Security Classification Wireless Communications System

User privacy protection is considered a critical issue in wireless networks, which drives the demand for various secure information interaction techniques. In this paper, we introduce an intelligent reflecting surface (IRS)-aided security classification wireless communication system, which reduces the transmit power of the base station (BS) by classifying users with different security requirements. Specifically, we divide the users into confidential subscribers with secure communication requirements and general communication users with simple communication requirements. During the communication period, we guarantee the secure rate of the confidential subscribers while ensuring the service quality of the general communication users, thereby reducing the transmit power of the BS. To realize such a secure and green information transmission, the BS implements a beamforming design on the transmitted signal superimposed with artificial noise (AN) and then broadcasts it to users with the assistance of the IRS's reflection. We develop an alternating optimization framework to minimize the BS downlink power with respect to the active beamformers of the BS, the AN vector at the BS, and the reflection phase shifts of the IRS. A successive convex approximation (SCA) method is proposed so that the nonconvex beamforming problems can be converted to tractable convex forms. The simulation results demonstrate that the proposed algorithm is convergent and can reduce the transmit power by 20\% compared to the best benchmark scheme.

Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-assisted Vehicular Networks

Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.