Mobile edge computing (MEC) has emerged as a promising technology to extend the functionality of end-users' wireless devices while prolonging their battery life by offloading computationally intensive tasks to remote edge servers. However, the inherent broadcast nature of wireless transmission during offloading introduces notable security challenges. To address this issue, we propose leveraging intelligent reflecting surface (IRS) technology to enhance physical layer security (PLS). Nevertheless, attaining high PLS for all users in dense networks with multiple malicious terminals is challenging. In this paper, we investigate the physical layer encryption (PLE) to complement the PLS in enabling secure wireless transmission. Since such encryption and decryption processes require computation resources, we aim to optimize the encryption decision, offloading decision, as well as wireless and computing resource allocations. Our objective is to minimize the maximum weighted energy consumption while satisfying practical constraints, including limited computing and wireless resources, fulfilling minimum user rate requirements, and complying with IRS conditions. To tackle the non-convex objective and constraints, we explore the utilization of bisection search and successive convex approximation (SCA) methods. Our numerical results confirm the efficiency of the proposed design in terms of energy consumption and network capacity within a secure MEC network.

peer reviewed

This paper presents a digital beamforming architecture based on the discrete Fourier transform, designed for medium-Earth orbit satellite payloads to serve multiple ground users. The system leverages a 16×16 16-point two-dimensional fast Fourier transform (2DFFT) to address the growing demand for high-speed data traffic and adaptable satellite communications. The architecture features a routing algorithm for flexible user allocation to any beam position and a cluster-based linear precoding approach to reduce resource and power consumption. Two versions of the 2DFFT module—quantized and non-quantized—are compared in terms of resource usage, power consumption, and performance. Experimental results show that the non-quantized version provides better power efficiency, while the quantized version removes the need for DSP blocks.

The escalating number of wireless users requiring different services, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC), has led to exploring non-orthogonal multiplexing methods like heterogeneous non-orthogonal multiple access (H-NOMA). This method allows users demanding divergent services to share the same resources. However, implementing the H-NOMA scheme faces major resource management challenges due to unpredictable interference caused by the random access mechanism of mMTC users. To address this issue, this paper proposes a joint optimization and cooperative multi-agent (MA) deep reinforcement learning-based resource allocation mechanism, aimed at maximizing the energy efficiency (EE) of H-NOMA-based networks. Specifically, this work initially establishes an optimization framework capable of determining the optimal power allocation for any specific sub-channel assignment (SA) setting for all users. Based on that, a cooperative MA double deep Q network (CMADDQN) scheme is carefully designed at the base station to conduct SA among users. In addition, a distributed full learning-based approach using MADDQN for both SA and power allocation is also designed for comparison purposes. Simulation results show that the proposed joint optimization and machine learning method outperforms the solely-learning-based approach and other benchmark schemes in terms of convergence rate and EE performance.

In this paper, we propose an anti-interception scheme to enhance the defence performance of frequency hopping (FH) tactical systems against the in-band full-duplex (IBFD) interception from the enemy. Our scheme utilizes an unmanned aerial vehicle (UAV)-based Intelligent Reflecting Surface (IRS) to share the interception burden with a FH system, thus mitigating jamming effects. To efficiently address IBFD interception, we jointly optimize the control of base station transmit power, FH hopping decision, and IRS phase shift adjustment. This joint optimization is mathematically formulated as a Mixed Integer Programming (MIP) non-convex optimization problem. To address the intractability of traditional optimization methods in solving this problem in modern tactical scenarios, we design a solution based on deep reinforcement learning (DRL). Extensive numerical results show that our proposed scheme significantly enhances the FH anti-interception capability and improves the QoS. Furthermore, the performance of our DRL solution is close to optimal and it is feasible to be deployed in modern practical scenarios.

The frequency hopping (FH) technique has always been crucial for anti-jamming tactical applications thanks to its advantages in avoiding the jammer's interception. However, modern tactical scenarios require FH systems to not only undertake defence missions but also meet increasingly high Quality of service (QoS) requirements. Unlike the prior works that mainly optimize FH systems by balancing anti-jamming capability and QoS performance, we propose a collaboration of FH and the intelligent reflecting surface (IRS) in an advanced anti-jamming scheme. Such approach shares the burden with the IRS and improves QoS. We formulate a joint IRS-aided FH anti-jamming problem as a Mixed Integer Programming (MIP) non-convex optimization. To address the intractability of traditional optimization methods in solving this problem in modern tactical scenarios, we design a solution based on deep reinforcement learning (DRL). The numerical results show that the performance of our solution is close to optimal, and it is scalable to be applicable in practical situations.

To address the explosion demand for data-intensive applications, enhancing wireless transmission capacity has become crucial for today’s networks. This paper focuses on improving the quality of service (QoS) and user satisfaction in intelligent reflecting surface (IRS)-assisted multicast multi-group systems by managing the actual transmitted data instead of sending all source data. A key challenge of this problem is determining the ergodic capacity when the signal-to-noise-plus-interference (SINR) distribution in IRS-assisted wireless systems is significantly complicated. To address this issue, we propose a deep neural network (DNN)-based framework to predict the long-term network capacity accurately. We adapt well-known zero-forcing (ZF) and block diagonalization (BD) techniques to achieve efficient and secure solutions in IRS-assisted multi-cast multi-group systems. Furthermore, we consider the system in case of imperfect channel state information (CSI). Adopting the three-phase channel estimation, we propose a two-stage learning framework to enhance the accuracy of the estimated channel. Based on predicted results, we investigate adjustment algorithms to adapt to environmental changes, thus increasing the received QoS and user satisfaction. Our numerical results confirm the efficiency of the proposed design, with the channel estimation error being significantly smaller than that of the three-phase channel estimation algorithm in the literature.

In this paper, we design a jamming mitigation plan to protect a mixed radio frequency/free-space optical (RF/FSO) relay network in the context that both RF and FSO systems are simultaneously attacked by enemy jammers. Our design aims to jointly optimize the power allocation (PA) and Field-of-View (FoV) tuning strategy to maximize the RF uplink sum rate subject to practical constraints on the jamming mitigation in both FSO and RF systems. In order to address the underlying non-convex optimization problem, we first derive the closed-form expression of the optimal Fo V angle. Then, the optimal FoV angle solution is used to solve the optimization PA. Since the PA problem has a non-convex form, we use an advanced technique of first-order Taylor approximation with difference of convex functions (D.C) method to solve it. Moreover, based on the Multi-Agent Deep Reinforcement Learning (MADRL) method, we develop a MADRL-based jamming mitigation algorithm to obtain the optimized solution of PA in near real-time. The numerical results show that the performance of the proposed MADRL-based jamming mitigation algorithm with low computational complexity is close to that of the optimization method.

We investigate the problem of dual protection for Warfighter Information Network-Tactical (WIN-T) of high-mobility ground combat vehicles (GCVs) against simultaneous energy-based and correlation-based interceptions. We design a joint resource optimization strategy in which the power allocation (PA) scheme controls transmit power, avoiding energy interception, and at the same time, the spreading factor assignment (SA) scheme manages correlation signal peaks to protect the network against the correlation analysis. We mathematically formulate this dual anti-interception resource allocation problem as a non-convex optimization model. We decompose this intractable optimization problem into two sub-problems, then solve the first sub-problem using an iterative method. To handle the non-convex form of the second sub-problem, we combine first-order Taylor approximation with the difference of convex functions (D.C) method. To obtain the optimized solution in near real-time, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach. The numerical results show that the performance of the low computational complexity MADRL is close to that of the optimization method. Thus, the MADRL method has the potential to be applicable in high-complexity military scenarios.

The imperfection of channel state information (CSI) estimation in intelligent reflecting surface (IRS)-assisted multi-user systems may heavily reduce the network capacity. Therefore, in this paper, we first investigate the two-stage learning channel estimation (2S-CE) framework to enhance the accuracy of the three-phase channel estimation (3P-CE) algorithm in the literature. Then, we manage the actual transmitted data instead of sending all source data in IRSs-assisted multi-cast multi-group (IRS-MC-MG) systems to achieve high quality of service (QoS) and user satisfaction. Well-known zero-forcing (ZF) and block diagonalization (BD) techniques are adapted to achieve high-efficient solutions in IRS-MC-MG systems. Finally, we investigate adjustment algorithms to adapt to the environmental change and the channel estimation (CE) imperfection, thus can increase the received QoS and user satisfaction. Numerical results show our proposed framework decreases more than 38 times of error compared with the literature method, i.e., the 3P-CE algorithm.

Semi-grant-free non-orthogonal multiple access (semi-GF NOMA) has emerged as a promising technology for the fifth-generation new radio (5G-NR) networks supporting the coexistence of a large number of random connections with various quality of service requirements. However, implementing a semi-GF NOMA mechanism in 5G-NR networks with heterogeneous services has raised several resource management problems relating to unpredictable interference caused by the GF access strategy. To cope with this challenge, the paper develops a novel hybrid optimization and multi-agent deep (HOMAD) reinforcement learning-based resource allocation design to maximize the energy efficiency (EE) of semi-GF NOMA 5G-NR systems. In this design, a multi-agent deep Q network (MADQN) approach is employed to conduct the subchannel assignment (SA) among users. While optimization-based methods are utilized to optimize the transmission power for every SA setting. In addition, a full MADQN scheme conducting both SA and power allocation is also considered for comparison purposes. Simulation results show that the HOMAD approach outperforms other benchmarks significantly in terms of the convergence time and average EE.

A fine power control policy and beam alignment is required between the base station (BS) and user equipment (UE) to achieve the promising performance of massive multiple input multiple output (MIMO) in millimeter wave (mmWave) communications. However, obtaining the channel state information (CSI) of mmWave - massive MIMO systems is challenging. In this paper, the beam-steering technique is used to estimate the signal strength from the BS to the user. We propose a novel learning framework to determine the suitable beam for a specific user and the transmit power for minimizing the cost including the transmit power and the unsatisfied rate when the channel is unknown. In addition, we address the missing data problem, and then employ the long-short term memory (LSTM) on the temporal processed inputs to select the suitable beam. Furthermore, we design a learning agent to predict the proper transmit power from the transmitted SSBs taking into account the required transmission rate. We then validate the proposed learning framework on the Deep MIMO dataset constructed based on accurate ray-tracing channels. Numerical results show our proposed framework outperforms the state-of-the-art prediction strategies, and approximates the best performance which is obtained when the CSI is available.

This paper aims to determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates jointly for the GEO satellite communication systems to minimize the payload power consumption and satisfy ground users' demands within a time window. Regarding constraint on the maximum number of illuminated beams per time slot, the technical requirement is formulated as a sparse optimization problem in which the hardware-related beam illumination energy is modeled in a sparsity form of the LP vectors. To cope with this problem, the compressed sensing method is employed to transform the sparsity parts into the quadratic form of pre-coders. Then, an iterative window-based algorithm is developed to update the LP vectors sequentially to an efficient solution. Additionally, two other two-phase frameworks are also proposed for comparison purposes. In the first phase, these methods aim to determine the MODCOD transmission schemes for users to meet their demands by using a heuristic approach or DNN tool. In the second phase, the LP vectors of each time slot will be optimized separately based on the determined MODCOD schemes.

In this paper, we investigate the problem of robot swarm control in 5G mission-critical robotic applications, i.e., in an automated grid-based warehouse scenario. Such application requires both the kinematic energy consumption of the robots and the ultra-reliable and low latency communication (URLLC) between the central controller and the robot swarm to be jointly optimized in real-time. The problem is formulated as a nonconvex optimization problem since the achievable rate and decoding error probability with short block-length are neither convex nor concave in bandwidth and transmit power. We propose a deep reinforcement learning (DRL) based approach that employs the deep deterministic policy gradient (DDPG) method and convolutional neural network (CNN) to achieve a stationary optimal control policy that consists of a number of continuous and discrete actions. Numerical results show that our proposed multi-agent DDPG algorithm achieves a performance close to the optimal baseline and outperforms the single-agent DDPG in terms of decoding error probability and energy efficiency.

Recently, cell-free massive multiple-input multiple-output (mMIMO), a distributed version of mMIMO, has increasingly been deployed to increase the spectral and energy efficiency of communication systems. In this paper, we investigate the ability of using cell-free mMIMO to reduce the jamming attacks which is a critical issue for communication. We propose a full-stack framework from detecting to suppressing jamming attacks in cell-free mMIMO system. At first, we exploit the unused pilots to design a jammer detector based on the likelihood functions of the measured signals. Then, we propose a jamming suppression method including two tasks: jamming estimation and access point (AP) selection. In the first task, we estimate the phase and amplitude when projecting the received signals onto the unused pilots, and then use them to estimate the jamming signal. In the second task, we employ the neural network-contextual multi-armed bandit (NN-CMAB) for online selection of APs which can provide the multi-user spatial diversity considering the existence of the jammers. Furthermore, we also propose a power control strategy for managing the minimum rate requirement in multi-user settings. Numerical results confirm the advantages of proposed designs over conventional jamming-ignorant-LMMSE strategy in spectral efficiency.

Intelligent offloading of computation-intensive tasks to a mobile cloud server provides an effective mean to expand the usability of wireless devices and prolong their battery life, especially for low-cost internet-of-things (IoT) devices. However, realization of this technology in multiple-input multiple-output (MIMO) systems requires sophisticated design of joint computation offloading and other network functions such as channel state information (CSI) estimation, beamforming, and resource allocation. In this paper, we study the computation task offloading and resource allocation optimization in MIMO based mobile edge computing systems considering perfect/imperfect-CSI estimation. Our design aims to minimize the maximum weighted energy consumption subject to practical constraints on available computing and radio resources and allowable latency. The optimal and low-complexity algorithms are proposed to solve the underlying mixed integer non-linear problems (MINLP). For the perfect-CSI, we employ bisection search to find the optimal solution. The low-complexity algorithms are developed by decomposing the original optimization problem into the offloading optimization (OP) and power allocation (PA) subproblems and solve them iteratively. Moreover, the difference of convex functions (DC) method is employed to deal with non-convex structure of (PA) subproblems in the imperfect-CSI scenario. Numerical results confirm the advantages of proposed designs over conventional local computation strategies in energy saving and fairness.

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC.

Intelligent offloading of computation-intensive tasks to a mobile edge computing server provides an effective mean to expand the usability of mobile devices and prolong their battery life. However, realization of this technology in today's multiple input multiple output (MIMO) wireless systems requires the sophisticated design of joint computation offloading and other communications functions such as channel state information (CSI) estimation and resource allocation. In this paper, we study the optimization of computation task offloading and resource allocation in MIMO wireless systems considering perfect and imperfect CSI estimation. Our design aims to minimize the maximum weighted energy consumption (Min-max W.C.E) subject to practical constraints on computing and radio resources and service latency. The optimal and sub-optimal algorithms are proposed to solve the underlying mixed integer non-linear problem (MINLP). In particular, the bisection search and difference of convex (DC) optimization methods are employed to determine the global and sub-optimal solutions for the perfect and imperfect CSI scenarios, respectively. Numerical results confirm the advantages of the proposed design over the conventional local computation strategy in handling computationally heavy tasks.

In this paper, we study the joint computation offloading and resource allocation problem in the two-tier wireless heterogeneous network (HetNet). Our design aims to optimize the computation offloading to the cloud jointly with the subchannel allocation to minimize the maximum (min-max) weighted energy consumption subject to practical constraints on bandwidth, computing resource and allowable latency for the multi-user multitask computation system. To tackle this non-convex mixed integer non-linear problem (MINLP), we employ the bisection search method to solve it where we propose a novel approach to transform and verify the feasibility of the underlying problem in each iteration. In addition, we propose a low-complexity algorithm, which can decrease the number of binary optimization variables and enable more scalable computation offloading optimization in the practical wireless HetNets. Numerical studies confirm that the proposed design achieves the energy saving gains about 55% in comparison with the local computation scheme under the strict required latency of 0.1s.

In this paper, we study the joint computation offloading and resource allocation problem exploiting computing resources from both mobile edge cloud and mobile peers. Our design aims to optimize the computation load assignments to local processors in the mobile users, mobile peers and the edge cloud jointly with the resource allocation to achieve the minimum weighted energy consumption subject to practical constraints on the bandwidth and computing resources and allowable latency. To tackle this non-convex optimization problem, we employ the successive convex approximation (SCA) method where we transform the underlying problem and iteratively solve a sequence of approximated convex problems. Moreover, the geometric programming (GP) method is applied to find the optimal solution of the approximated problem. The proposed SCA-based approach employs the arithmetic-geometric mean (AGM) approximation and the proposed algorithm is proved to converge to a local optimal solution. Finally, numerical studies confirm that the proposed scheme achieves energy saving gains about 60% and 10% in comparison with the local computation strategy and cloud offloading strategy under the strict required latency of 0.25s, respectively.