In recent years, federated learning (FL), a promising machine learning technique, has drawn increasing attention. Unlike conventional methods that centralize data for model training, FL ensures the safety and confidentiality of data by enabling the model training process on intelligent devices. However, the unpredictable and continuously changing channel characteristics pose a significant challenge to the implementation of FL in wireless communication. Consequently, optimizing energy efficiency becomes crucial, particularly for devices with constrained power resources. In this study, we tackle the problem of minimizing the total energy consumption during the training phase of FL over a cell-free massive multiple-input multiple-output (CFMM) system assisted by intelligent reflecting surfaces (IRSs) composed of hardware-efficient passive elements. Based on the operation mode of user equipments (UEs) during the uplink phase, the proposed CFMM-IRS system is categorized into System I employing time division multiple access mode, and System II allowing simultaneous user operation. Distinct problems are formulated for these systems, aiming to minimize all UEs’ overall energy consumption. To deal with these non-convex complex problems, we propose an alternating optimization approach by transforming the original problems into three sub-optimal ones and designing iterative algorithms utilizing the inner approximation method. Extensive experiments show the effectiveness of the proposed alternating optimization approach when resolving the given complex problems, highlighting their superior performance compared to other benchmark algorithms. Finally, simulation results indicate that System II, with UEs operating in simultaneous mode and associated with the proposed alternating algorithm, achieves the best result in terms of energy efficiency.

This paper proposes a power allocation framework based on the per-user quality-of-experience (QoE) conditions for an aerial relay massive MIMO (mMIMO) network, assuming the direct transmission between the mMIMO base station and ground users (UEs) is unavailable. We first derive closed-form spectral efficiency expressions for the mMIMO-based system, with a UAV acting as the aerial relay. Then, we formulate a joint optimization problem of power allocation and QoE in the downlink, aiming to maximize the sum throughput of the serving ground UEs. The problem is generally hard to solve due to the non-convex constraints and non-concave objective function. To address it, we propose a two-step algorithm based on inner convex approximation (ICA) method. However, the ICA-based algorithm requires an initial feasible point, which is difficult to find by generating a random point as commonly conceived in existing approaches while satisfying the QoE constraints. To tackle this issue, we develop a max-min problem, whose solution leads to an initial feasible point that maximizes the difference between the per-user rate and its corresponding QoE threshold. Numerical results are used to demonstrate the validity of the theoretical analysis and the effectiveness of the proposed algorithms.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

This paper proposes a flexible and efficient access control scheme that combines the orthogonal frequency division multiple access and rate-splitting multiple-access techniques for enhancing the uplink transmission in a digital twin edge network system. We formulate a non-convex mixed integer optimization problem that minimizes the energy consumption of all Internet of Things devices (IoTDs) and maximizes the number of successful IoTD tasks. To this end, we propose a deep reinforcement learning (DRL) framework by normalizing a DRL training algorithm named deep deterministic policy gradient for efficiently designing the variables while ensuring the problem constraints. However, in the inference stage, the proposed DRL method may encounter different devices and services. Therefore, we design an exhaustive-improved DRL method that can improve the proposed DRL effectively using information from a digital-twin module. We also propose a mathematical approximation-based solution employing two convexification approach: Dinkelbach’s method and relaxed Linear Matrix Inequality (LMI). Through extensive simulations over different parameters and scenarios, we identify the polynomial complexity, stable convergence, and operating regime of the proposed solutions. It is also confirmed that the proposed approaches work well even with digital twin defects and provide improved performance in terms of energy consumption and number of successful tasks in comparison with benchmark schemes.

Spectral efficiency (SE) and energy efficiency (EE) are crucial for next-generation wireless Internet of Things networks. Device-to-device (D2D) communications and sparse code multiple access (SCMA) are promising solutions to meet the high Quality of Service. This requires a proper interference management for an ultradense user system. Considering both resource and power allocation, we herein aim to optimize the SE and improve the EE. First, rate fairness is considered to assign SCMA codewords to both cellular and D2D users. Subsequently, we propose a beamforming design and power allocation algorithm for D2D communications underlaying downlink and uplink operation of cellular networks, wherein energy harvesting is involved as a simultaneous wireless information and power transfer to assist an SCMA network.

A cell-free (CF) massive multiple-input-multiple-output (mMIMO) system can provide uniform spectral efficiency (SE) with simple signal processing. On the other hand, a recently introduced technology called hybrid relay-reflecting intelligent surface (HR-RIS) can customize the physical propagation environment by simultaneously reflecting and amplifying radio waves in preferred directions. Thus, it is natural that incorporating HR-RIS into CF mMIMO can be a symbiotic convergence of these two technologies for future wireless communications. This motivates us to consider an HR-RIS-aided CF mMIMO system to utilize their combined benefits. We first model the uplink/downlink channels and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis of SE performance of the considered system. Specifically, we derive closed-form expressions for the uplink and downlink SE. The results reveal important observations on the performance gains achieved by HR-RISs compared to conventional systems. The presented analytical results are also valid for conventional CF mMIMO systems and those aided by passive reconfigurable intelligent surfaces. Such results play an important role in designing new transmission strategies and optimizing HR-RIS-aided CF mMIMO systems. Finally, we provide extensive numerical results to verify the analytical derivations and the effectiveness of the proposed system design under various settings.

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to validate the obtained analytical framework. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks.

We consider in this work a cell-free (CF) massive multiple-input-multiple-output (mMIMO) system where multiple hybrid relay-reflecting intelligent surfaces (HR-RIS) are deployed to assist communication between access points and users. We first present the signal model and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis for the considered HR-RIS-aided CF mMIMO system, where the closed-form expression of the downlink throughput is derived. The presented analytical results are also valid for conventional CF mMIMO systems, i.e., CF mMIMO systems with and without passive reconfigurable intelligent surfaces. Finally, the analytical derivations are verified by extensive numerical results.

This paper investigates the combined benefits of full-duplex (FD) and cell-free massive multiple-input multiple-output (CF-mMIMO), where a large number of distributed access points (APs) having FD capability simultaneously serve numerous uplink and downlink user equipments (UEs) on the same time-frequency resources. To enable the incorporation of FD technology in CF-mMIMO systems, we propose a novel heap-based pilot assignment algorithm, which not only can mitigate the effects of pilot contamination but also reduce the involved computational complexity. Then, we formulate a robust design problem for spectral efficiency (SE) maximization in which the power control and AP-UE association are jointly optimized, resulting in a difficult mixed-integer nonconvex programming. To solve this problem, we derive a more tractable problem before developing a very simple iterative algorithm based on inner approximation method with polynomial computational complexity. Numerical results show that our proposed methods with realistic parameters significantly outperform the existing approaches in terms of the quality of channel estimate and SE.

In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CF-mMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., small-cell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining.

In this paper, physical layer security is considered for cognitive radio networks using an unmanned aerial vehicle (UAV)-enabled jamming noise. In the studied model, a secondary transmitter sends confidential messages to a secondary receiver in the presence of an external eavesdropper (Eve), and the UAV acts as a friendly jammer that degrades the decoding capability of Eve. Therefore, resource allocation in such a network must jointly optimize the transmission power and UAV's trajectory to maximize the secrecy rate, while satisfying a given interference threshold at the primary receiver. The design problem is non-convex, and thus, global optimality is difficult to obtain. Aiming to solve this problem, we first transform it into a more tractable form, and then propose a successive convex approximation-based algorithm for its solutions. The proposed algorithm has a low computational complexity and is guaranteed to obtain at least a locally optimal solution of the original problem. Numerical results are provided to demonstrate the effectiveness of the proposed design, compared to the existing ones.

This paper investigates the coexistence of non- orthogonal multiple access (NOMA) and full-duplex (FD), where the NOMA successive interference cancellation technique is applied simultaneously to both uplink (UL) and downlink (DL) transmissions in the same time-frequency resource block. Specifically, we jointly optimize the user association (UA) and power control to maximize the overall sum rate, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize the UL users' decoding order and the DL users' clustering, which results in a mixed-integer non- convex problem. For solving this problem, we first relax the binary variables to be continuous, and then propose a low-complexity design based on the combination of the inner convex approximation framework and the penalty method. Numerical results show that the proposed algorithm significantly outperforms the conventional FD-based schemes, FD-NOMA and its half-duplex counterpart with random UA.

This paper investigates the coexistence of non-orthogonal multiple access (NOMA) and full-duplex (FD) to improve both spectral efficiency (SE) and user fairness. In such a scenario, NOMA based on the successive interference cancellation technique is simultaneously applied to both uplink (UL) and downlink (DL) transmissions in an FD system. We consider the problem of jointly optimizing user association (UA) and power control to maximize the overall SE, subject to user-specific quality-of-service and total transmit power constraints. To be spectrally-efficient, we introduce the tensor model to optimize UL users’ decoding order and DL users’ clustering, which results in a mixed-integer non-convex problem. For practically appealing applications, we first relax the binary variables and then propose two low-complexity designs. In the first design, the continuous relaxation problem is solved using the inner convex approximation framework. Next, we additionally introduce the penalty method to further accelerate the performance of the former design. For a benchmark, we develop an optimal solution based on brute-force search (BFS) over all possible cases of UAs. It is demonstrated in numerical results that the proposed algorithms outperform the conventional FD-based schemes and its half-duplex counterpart, as well as yield data rates close to those obtained by BFS-based algorithm.

This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the base station with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max-min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. The simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.

Consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to concurrently serve both downlink and uplink users in the presence of half-duplex eavesdroppers (Eves). The target problem is to maximize the minimum secrecy rate (SR) among all legitimate users. A novel user grouping-based fractional time allocation is proposed as an alternative solution, where information signals at the FD-BS are accompanied by artificial noise to degrade the Eves' channels. The SR problem has a highly non-concave and non-smooth objective, subject to non-convex constraints due to coupling between the optimization variables. Nevertheless, we develop a path-following low- complexity algorithm, which involves only a simple convex program of moderate dimensions at each iteration. Numerical results demonstrate the merit of the proposed approach compared to existing well-known ones, i.e., conventional FD and FD non-orthogonal multiple access.

This paper considers device-to-device (D2D) communications underlaying an uplink cellular network that adopts sparse code multiple access technology. We aim to maximize the spectral efficiency for D2D links in a transmission block via a joint power and resource allocation subject to the quality-of-service requirements for both cellular user equipments and D2Ds. We propose a low-complexity two-phase algorithm based on heuristic search and inner approximation method to efficiently solve the problem.

The authors investigate pilot contamination in a multicell massive multiple‐input multiple‐output system, in which the estimated channel state information (CSI) is significantly different from the real CSI. Pilot contamination is an obstacle to achievable rates for users. The authors first propose an uplink training strategy to limit the effect of intercell pilot contamination on channel estimation with low complexity. As a result, the downlink data rate through the reciprocal channels is significantly improved. However, as the conventional uplink training, this strategy suffers from intra‐cell pilot contamination when the length of pilots is smaller than the number of users in a cell, while increasing the length of pilots degrades the spectral efficiency. The authors, therefore, propose a pilot optimisation that can be incorporated into the proposed training strategy. Based on the minimum mean square error criterion, they derive a closed‐form equation to determine the optimal pilots for all users in a multicell system. The joint pilot optimisation and proposed uplink training strategy is found to outperform other schemes, especially when the number of users exceeds the length of pilots in a cell. Numerical results verify that the downlink achievable rate with the proposed training strategy and pilot optimisation outperforms that with conventional approaches.

We consider a full-duplex (FD) multiuser system where an FD base station (BS) is designed to simultaneously serve both downlink (DL) and uplink (UL) users in the presence of half-duplex eavesdroppers (Eves). The problem is to maximize the minimum (max-min) secrecy rate (SR) among all legitimate users, where the information signals at the FD-BS are accompanied with artificial noise to debilitate the Eves' channels. To enhance the max-min SR, a major part of the power budget should be allocated to serve the users with poor channel qualities, such as those far from the FD-BS, undermining the SR for other users, and thus compromising the SR per-user. In addition, the main obstacle in designing an FD system is due to the self-interference (SI) and co-channel interference (CCI) among users. We therefore propose an alternative solution, where the FD-BS uses a fraction of the time block to serve near DL users and far UL users, and the remaining fractional time to serve other users. The proposed scheme mitigates the harmful effects of SI, CCI, and multiuser interference, and provides system robustness. The SR optimization problem has a highly nonconcave and nonsmooth objective, subject to nonconvex constraints. For the case of perfect channel state information (CSI), we develop a low-complexity path-following algorithm, which involves only a simple convex program of moderate dimension at each iteration. We show that our path-following algorithm guarantees convergence at least to a local optimum. Then, we extend the path-following algorithm to the cases of partially known Eves' CSI, where only statistics of CSI for the Eves are known, and worst-case scenario in which Eves can employ a more advanced linear decoder. The merit of our proposed approach is further demonstrated by extensive numerical results.

This paper considers a full-duplex system for a base station (BS) serving both uplink and downlink users simultaneously on the same frequency. A new design of the selection of the half-array antenna mode at the BS (transmit or receive mode) over the time phases and user assignments is proposed and its beamforming design and power allocation problem are optimized under the effect of both residual self- interference and co-channel interference. The aim is to maximize the overall sum rate subject to the users' quality of service requirements, which is formulated as a highly nonlinear function subject to non-convex constraints. To solve this non-convex problem, we propose an iterative low- complexity algorithm. Simulation results demonstrate that the proposed algorithm provides a fast convergence and substantially outperforms all existing schemes.

Pilot contamination reduction can improve the achievable uplink and downlink rates in a multicell massive multiple‐input–multiple‐output system. To this end, this study first proposes two schemes to mitigate the effect of pilot contamination by implementing uplink training with time‐aligned and time‐shifted pilot transmissions. Accordingly, these techniques enable the system not only to exploit the advantages of time‐shifted training but also to benefit from the use of conventional systems that improve the achievable rates. The authors introduce channel estimation in matrix form by estimating from a near minimum mean square error method. Furthermore, linear detector and precoding methods are used for a base station with a finite number of antennas in order to derive the closed‐form lower bounds of the achievable rates with two uplink training schemes for both uplink and downlink transmissions. Finally, numerical results are presented to verify the analysis.