In this paper, fresh Basic Safety Messages (BSM) (e.g., vehicle’s position and speed) are used to control the Connected Automated Vehicles (CAVs) to reduce Time to Collision (TTC) error which leads to decrease in Collision Risk (CR). In contrast to exiting works, a novel Safety-aware Age of Information (S-AoI) metric is proposed that in addition to AoI, takes into account the risk assessment of CAVs to design an efficient transmission protocol for BSMs. We also deploy user-centric Cell-free-massive-MIMO (CFmMIMO) to improve the communication coverage, accessibility, and reliability, where each CAV is served by a cluster of nearby Access Points (APs). Unlike previous works, a two time-scale distributed deterministic policy gradients algorithm is adopted which greatly reduces the signal processing complexity, system load as well as signaling overhead while maintaining the performance. Simulation results show that the proposed framework, i.e, user-centric CFmMIMO technology together with S-AoI metric, can reduce average TTC error between 24%-35% across different lane change probabilities compared to the baseline scenario in which we use small cell mMIMO with AoI metric. Such a reduction in TTC error results in significant decrease (as high as 75%) in CR ratio.

In delay-sensitive industrial Internet of Things (IIoT) applications, the age of information (AoI) is employed to characterize the freshness of information. Meanwhile, the emerging network function virtualization provides flexibility and agility for service providers to deliver a given network service using a sequence of virtual network functions (VNFs). However, suitable VNF placement and scheduling in these schemes is NP-hard and finding a globally optimal solution by traditional approaches is complex. Recently, deep reinforcement learning (DRL) has appeared as a viable way to solve such problems. In this paper, we first utilize single agent low-complex compound action actor-critic RL to cover both discrete and continuous actions and jointly minimize VNF cost and AoI in terms of network resources under end-to-end Quality of Service constraints. To surmount the single-agent capacity limitation for learning, we then extend our solution to a multi-agent DRL scheme in which agents collaborate with each other. Simulation results demonstrate that single-agent schemes significantly outperform the greedy algorithm in terms of average network cost and AoI. Moreover, multi-agent solution decreases the average cost by dividing the tasks between the agents. However, it needs more iterations to be learned due to the requirement on the agents' collaboration.

This paper studies a novel dual-mode scheduling framework that jointly performs power allocation, beamforming, and sparse code multiple access (SCMA) based scheduling over microwave and millimeter wave (mmW) bands. We propose a robust secure transmission scheme assuming imperfect channel state information for the eavesdropper links. The proposed scheduling framework allows users to schedule simultaneously on each dual-mode BS, based on SCMA, to maximize the joint access secrecy and backhaul rates under transmit power constraints. It is shown that the proposed scheduling framework can find an effective scheduling solution over both microwave and mmW in polynomial time. Simulation results show that the dual connectivity, and joint solutions have 22.5%, and 20% performance gain compared to only microwave, and the disjoint solution, respectively.

We propose a robust resource allocation framework to improve the physical layer security in the presence of an active eavesdropper. In the considered system, we assume that both legitimate receiver and eavesdropper are full-duplex (FD) while most works in the literature concentrate on passive eavesdroppers and half-duplex (HD) legitimate receivers. In this paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the secrecy data rate of the legitimate transmission. In the literature, assuming that the receiver operates in HD mode, secrecy data rate maximization problems subject to the power transmission constraint have been considered in which cooperating nodes act as jammers to confound the eavesdropper. This paper investigates an alternative solution in which we take advantage of FD capability of the receiver to send jamming signals against the eavesdroppers. The proposed self-protection scheme eliminates the need for external helpers. Moreover, we consider the channel state information uncertainty on the links between the active eavesdropper and other legitimate nodes of the network. Optimal power allocation is then obtained based on the worst-case secrecy data rate maximization, under a legitimate transmitter power constraint in the presence of the active eavesdropper. Numerical results confirm the advantage of the proposed secrecy design and in certain conditions, demonstrate substantial performance gain over the conventional approaches.

In this paper, we consider the problem of resource allocation for secure communications in decode-and-forward (DF) relay-assisted orthogonal frequency-division multiple access (OFDMA) networks. In our setting, users want to securely communicate to the base station (BS) with the help of a set of relay stations (RSs) in the presence of multiple eavesdroppers. We assume that all channel state information (CSI) of the legitimate links and only the channel distribution information (CDI) of the eavesdropper links are available. We formulate our problem as an optimization problem whose objective is to maximize the sum secrecy rate of the system subject to individual transmit power constraint for each user and RS. As a first work which considers limited feedback schemes for secure communications in cooperative OFDMA networks, we consider the limited-rate feedback case, where in addition to transmit power and subcarrier assignments, channel quantization should be performed and boundary regions of channels should be computed. We further consider the noisy feedback channel. We solve our problem using the dual Lagrange approach and propose an iterative algorithm whose convergence is analyzed. Using simulations, we evaluate the performance of the proposed scheme in numerous situations.

We propose a robust resource allocation framework to provide physical layer security for a multiple input single output (MISO) communication system. In the considered system, we assume that the both legitimate receiver and eavesdropper are in full-duplex (FD) mode and compare the corresponding performance to conventional cooperative jamming frameworks where a half-duplex (HD) receiver is at hand. In the present paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the MISO secrecy rate between the legitimate transmitter and receivers. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. Moreover, we investigate robustness against channel state information uncertainty. Optimal power allocation is obtained based on worst-case secrecy rate maximization, under legitimate transmitter power constraint in the presence of an active eavesdropper. Numerical results are then provided to confirm the advantages of using FD receivers.

In this paper, the effect of channel distribution information (CDI) imperfectness on the performance of wireless networks is investigated. In the literature, ergodic resource allocation problems are solved assuming that perfect CDI is available which might not be a practical assumption. Therefore, we adopt the nonparametric density estimation methods for estimating the channel gain distribution. The estimation is first carried out through the well-known kernel density estimation (KDE) method. Since KDE is too sensitive to contaminated data, we adopt the robust kernel density estimation (RKDE) method. The analysis is performed over an ergodic resource allocation problem framework in the uplink of an orthogonal frequency division multiple access based network. In the proposed problem, the objective is to maximize the average total rate subject to total power constraint for each user. Simulation results indicate that for large enough number of nominal data and a reasonable number of outlier data, RKDE can provide a sum rate very close to the one obtained based on the actual CDI.

Femtocell networks are expected to offer significant performance improvement with low cost. However, adaptive transmission based on maximising the sum rate of macro and femto networks requires perfect channel quality information (CQI) of the macro and femto links. This assumption requires an infinite resolution feedback link, which is not always practical since it requires an excessive amount of bandwidth. This study considers the problem of maximising average sum rate under the constraint of average maximum power transmission at macro and femto users. A suboptimal iterative algorithm is developed for finding the optimal CQI quantisers as well as the discrete power and rate at macro and femto transmitter for each quantised CQI level so as to maximise the average sum rate of the system. The author's numerical results give the number of bits required to sufficiently represent the CQI to achieve almost the maximum sum rate attained using full knowledge of the CQI.