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.

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.

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.

Reducing waiting time due to scheduling process and exploiting multi-access transmission, grant-free non-orthogonal multiple access (GF-NOMA) has been considered as a promising access technology for URLLC-enabled 5G system with strict requirements on reliability and latency. However, GF-NOMA-based systems can suffer from severe interference caused by the grant-free (GF) access manner which may degrade the system performance and violate the URLLC-related requirements. To overcome this issue, the paper proposes a novel reinforcement-learning (RL)-based random access (RA) protocol based on which each device can learn from the previous decision and its corresponding performance to select the best subchannels and transmit power level for data transmission to avoid strong cross-interference. The learning-based framework is developed to maximize the system access efficiency which is defined as the ratio between the number of successful transmissions and the number of subchannels. Simulation results show that our proposed framework can improve the system access efficiency significantly in overloaded scenarios.

In this paper, we investigate the multiplexing of grant-based (GB) and grant-free (GF) device transmissions in an uplink heterogeneous network (HetNet), namely GB-GF HetNet, where the devices transmit their information using low-rate short data packets. Specifically, GB devices are granted unique time-slots for their transmissions. In contrast, GF devices can randomly select time-slots to transmit their messages utilizing the GF non-orthogonal multiple access (NOMA), which has emerged as a promising enabler for massive access and reducing access latency. However, random access (RA) in the GF NOMA can cause collisions and severe interference, leading to system performance degradation. To overcome this issue, we propose a multiple access (MA) protocol based on reinforcement learning for effective RA slots allocation. The proposed learning method aims to guarantee that the GF devices do not cause any collisions to the GB devices and the number of GF devices choosing the same time-slot does not exceed a predetermined threshold to reduce the interference. In addition, based on the results of the RA slots allocation using the proposed method, we derive the approximate closed-form expressions of the average decoding error probability (ADEP) for all devices to characterize the system performance. Our results presented in terms of access efficiency (AE), collision probability (CP), and overall ADEP (OADEP), show that our proposed method can ensure a smooth operation of the GB and GF devices within the same network while significantly minimizing the collision and interference among the device transmissions in the GB-GF HetNet.

In massive machine-type communications (mMTC) networks, the ever-growing number of MTC devices and the limited radio resources have caused a severe problem of random access channel (RACH) congestion. To mitigate this issue, several potential multiple access (MA) mechanisms including sparse code MA (SCMA) have been proposed. Besides, the short-packet transmission feature of MTC devices requires the design of new transmission and congestion avoidance techniques as the existing techniques based on the assumption of infinite data-packet length may not be suitable for mMTC networks. Therefore, it is important to find novel solutions to address RACH congestion in mMTC networks while considering SCMA and short-packet communications (SPC). In this paper, we propose an SCMA-based random access (RA) method, in which Q-learning is utilized to dynamically allocate the SCMA codebooks and time-slot groups to MTC devices with the aim of minimizing the RACH congestion in SPC-based mMTC networks. To clarify the benefits of our proposed method, we compare its performance with those of the conventional RA methods with/without Q-learning in terms of RA efficiency and evaluate its convergence. Our simulation results show that the proposed method outperforms the existing methods in overloaded systems, i.e., the number of devices is higher than the number of available RA slots. Moreover, we illustrate the sum rate comparison between SPC and long-packet communications (LPC) when applying the proposed method to achieve more insights on SPC.

The ever-increasing number of machine-type communications (MTC) devices and the limited available radio resources are leading to a crucial issue of radio access network (RAN) congestion in upcoming 5G and beyond wireless networks. Thus, it is crucial to investigate novel techniques to minimize RAN congestion in massive MTC (mMTC) networks while taking the underlying short-packet communications (SPC) into account. In this paper, we propose an adaptive Q-learning (AQL) algorithm based on block error rate (BLER), an important metric in SPC, for a non-orthogonal multiple access (NOMA) based mMTC system. The proposed method aims to efficiently accommodate MTC devices to the available random access (RA) slots in order to significantly reduce the possible collisions, and subsequently to enhance the system throughput. Furthermore, in order to obtain more practical insights on the system design, the scenario of imperfect successive interference cancellation (ISIC) is considered as compared to the widely-used perfect SIC assumption. The performance of the proposed AQL method is compared with the recent Q-learning solutions in the literature in terms of system throughput over a range of parameters such as the number of devices, blocklength, and residual interference caused by ISIC, along with its convergence evaluation. Our simulation results illustrate the superiority of the proposed method over the existing techniques, in the scenarios where the number of devices is higher than the number of available RA time-slots.

This paper proposes a new communication protocol for a cooperative non-orthogonal multiple access (NOMA) system. In this system, based on users' channel conditions, each two NOMA users are paired to reduce system complexity. In this concern, the user with a better channel condition decodes and then forwards messages received from the source to the user with a worse channel condition. In particular, the direct link between the paired users is assumed to be unavailable due to the weak transmission conditions. To overcome this issue, we propose a new cooperative NOMA protocol in which an amplify-and-forward (AF) relay is employed to help the user-to-user communications. To evaluate the proposed protocol, the exact closed-form expressions of outage probability (OP) at the two paired users are derived. Based on the analysis of the OP, we further examine the system throughput in a delay-sensitive transmission mode. Finally, our analytical results verified by Monte-Carlo simulation show that the proposed protocol is efficient in enhancing the performance of NOMA system when the user-to-user communications is limited.

This paper analyzes a novel multiple-input single- output (MISO) simultaneous wireless information and power transfer (SWIPT) system model in the presence of an active eavesdropper over the frequency-selective fading channel. In this model, a transmitter applies the time reversal (TR) beamforming technique to combat the fading effects whereas a legitimate user employs a power splitter to jointly receive information and energy, and the active eavesdropper jams the user. Given the system model, the system performance in terms of symbol error rate (SER) and energy harvesting (EH) is analyzed. In particular, we devise a SER-energy region, instead of the conventional rate-energy region, to evaluate system performance of the SWIPT model. A moment generating function (MGF)-based method is presented to exactly derive the average SER analysis. Moreover, the closed-form expression of average effective harvested energy is then provided to complete SERenergy region analysis. Finally, analytical results confirmed by numerical simulations show that the TR technique can support the SWIPT system to notably improve the SER performance whereas the jamming signal can enhance the EH performance.

In this paper, we investigate the performance of an amplify-and-forward two-way relay network, in which the relay can harvest the energy from the radio frequency and all channels are subject to Nakagami-m fading. In particular, we assume that the relay has a single antenna, while two sources are equipped with multiple antennas. Also, two sources use the maximal ratio transmission (MRT) and maximal ratio combining (MRC) techniques to process the transmit and received signals. Moreover, the multiple access broadcast (MABC) protocol, which can improve the spectrum efficiency, is employed to coordinate the bidirectional communication of the two-way relay network. Given these settings, we derive analytical expressions for the throughput under the delay-limited and delay-tolerant transmission modes and the outage probability. Finally, Monte-Carlo simulations are provided to verify our calculation.

Based on the capability of harvesting the energy of all paths in the multipath environment, time-reversal (TR) transmission technique offers a great potential of low-complexity energy-efficient communications for future wireless network. The TR-employed systems benefit from a significantly reduced leakage of message-bearing signal to unintended users or eavesdroppers. In this paper, we propose a term so-called average effective secrecy signal-to-interference-plus-noise ratio (SINR) which represents the secrecy performance of TR-employed systems. Furthermore, we also consider: (i) the channel correlation between transmit antennas, and (ii) the channel correlation between a legitimate user and an eavesdropper. Accordingly, we derive the average effective secrecy-SINR in a closed-form expression of TR-employed systems. The obtained expression is based on the exact power-averaged expressions of the desired signal and the inter-symbol interference (ISI) components at both the legitimate user and eavesdropper side. The analytical results confirmed by numerical simulations show that the average effective secrecy-SINR metric is preferred to the average secrecy-SINR term in the perspective of measuring secrecy performance over correlated multi-path channel.