Search

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep reinforcement learning (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.

Reconfigurable intelligent surface (RIS)-aided wireless communications is a hot research topic in academic and industry communities since it can enhance both the spectrum and energy efficiency of wireless systems by artificially reconfiguring the wireless propagation environment. An RIS can configure tiny antenna elements or scatterers, which can be judiciously tuned to enhance the signal power for desired users, such as primary users in cognitive radio networks, or suppress the signal power for undesired users, such as eavesdroppers for physical layer security. RISs also find promising applications in dense urban areas or indoor scenarios, where the electromagnetic waves are prone to be blocked by obstacles such as buildings and walls. There are numerous advantages associated with RISs. For instance, since an RIS needs no analog-to-digital converters or radiofrequency chains, it reduces the power consumption, hence improving sustainability and reducing cost. RISs can be fabricated at a limited complexity and cost, and they can be easily deployed on buildings facades, walls, ceilings, and streetlamps. Furthermore, RISs can be readily integrated into current wireless networks (both cellular networks and WiFi). Due to these appealing advantages, RIS is envisioned to be one of the key technologies for the sixth-generation (6G) wireless networks.

This paper studies a full-duplex symbiotic radio network, where a full-duplex access point (FAP) transmits downlink orthogonal frequency division multiplexing signals to a legacy user (LU) and simultaneously receives signals backscattered from multiple passive backscatter devices (BDs) with capability of radio-frequency energy harvesting. A non-orthogonal-multiple-access enhanced dynamic-time-division-multiple-access (NOMA-DTDMA) transmission scheme is proposed to exploit the channel dynamics and further improve the spectrum efficiency. In order to maximize the throughput performance and ensure BD fairness, we maximize the minimum throughput among all BDs by jointly optimizing the FAP's subcarrier power allocation, the BDs' backscatter time allocation and power reflection coefficients, subject to the LU's throughput requirement, the BDs' harvested energy requirements, and other practical constraints. An efficient iterative algorithm is proposed to solve the formulated non-convex problem, by utilizing the block coordinated descent and successive convex optimization techniques. The convergence and complexity of the proposed algorithm are also analyzed. Numerical results show that the proposed NOMA-DTDMA scheme significantly outperforms the benchmark scheme of dynamic TDMA in terms of both throughput performance and BD fairness. Also, the trade-off performances between the BDs' throughput and the LU's throughput requirement as well as the BDs' harvested energy requirements are numerically verified.

Over the past decade, blockchain technology has attracted tremendous attention from both academia and industry. The popularity of blockchains was originated from the concept of crypto-currencies to serve as a decentralized and tamper-proof transaction data ledger. Nowadays, blockchains as the key framework in the decentralized public data-ledger have been applied to a wide range of scenarios far beyond crypto-currencies, such as the Internet of Things, healthcare, and insurance. This survey aims to fill the gap between a large number of studies on blockchain networks, where game theory emerges as an analytical tool, and the lack of a comprehensive survey on the game theoretical approaches applied in blockchain-related issues. In this survey, we review the game models proposed to address common issues in the blockchain network. The focus is placed on security issues, e.g., selfish mining, majority attack and denial of service attack, issues regarding mining management, e.g., computational power allocation, reward allocation, and pool selection, as well as issues regarding blockchain economic and energy trading. Additionally, we discuss the advantages and disadvantages of these selected game theoretical models and solutions. Finally, we highlight important challenges and future research directions of applying game theoretical approaches to incentive mechanism design and the combination of blockchain with other technologies.

This paper investigates the problem of power allocation for distributed estimation via diffusion in wireless sensor networks (WSNs) with simultaneous wireless information and power transfer (SWIPT). We consider a WSN consisting of smart sensor nodes (SNs) and common sensor nodes (CNs), and each SN is capable of performing SWIPT via multi-antenna beamforming to its neighboring (i.e., near-tier) CNs. In each diffusion iteration, all nodes collect measurements and exchange intermediate estimates with their neighbors. We first analyze the effect of each SN's beamforming design and each near-tier CN's harvested power allocation on the steady-state network-wide mean square deviation (MSD) of the diffusion least-mean-squares (LMSs) strategy. Then, we formulate a problem to minimize an upper bound MSD by jointly optimizing the global power allocation weights for each SN to perform beamforming, and the local power allocation proportion for each CN to perform measurement collection. We further show that the formulated non-convex problem is decomposable and propose a gradient-based iterative algorithm to find the optimal solution. In addition, for practical implementation, we propose adaptive online approaches to estimate some parameters required for system optimization. Finally, extensive simulation results demonstrate that with optimal power allocation, our proposed scheme improves the MSD performance significantly, compared to the conventional diffusion LMS strategy without wireless power transfer (WPT).

In this paper, we investigate the resource allocation problem for the symbiotic radio system, in which the passive backscatter transmission is parasitic in the primary transmission, and the cooperative receiver simultaneously detects the signals transmitted from the active primary transmitter (PT) and the backscatter device (BD). In particular, channel fading is considered, and the ergodic weighted sum rate of the primary and backscatter transmissions is maximized by jointly optimizing the transmit power at the PT and the reflection coefficient at the BD under either long-term or short-term transmit-power constraint over the fading states. Two practical transmission setups are considered, where the relationship between the primary and backscatter transmissions is either commensal or parasitic. For the commensal setup, we fix the reflection coefficient as the maximum value and derive the optimal power allocation scheme based on the standard convex optimization procedure. For the parasitic setup, the weighted sum-rate maximization problem is non-convex, and we resort to the concave-convex procedure to derive a suboptimal solution. Simulation results demonstrate that in the commensal setup, the BD may realize transmission while causing no harmful interference to the primary system; in the parasitic setup, a higher backscatter data rate can be achieved through slightly sacrificing the primary transmission rate.

In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in the desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system, where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms.

In this paper, we are interested in a symbiotic radio (SR) system, in which a passive full-duplex backscatter device (BD) is parasitic in an active primary transmission. The primary transmitter (PT) with multiple antennas is designed to broadcast common messages to the primary receiver (PR) and the BD, as well as to support passive information transmission from the BD to the PR. To do so, the full-duplex BD absorbs a fraction of the incident signal from the PT to decode the common messages and simultaneously transmits its own information to the PR by backscattering the remaining part of the incident signal. We derive the achievable rates of the BD transmission with Gaussian and quadrature amplitude modulation codewords. We also formulate a transmit power minimization problem by jointly designing the beamforming vector at the PT and the power splitting factor at the BD. This problem is first solved by the semi-definite relaxation technique together with a one-dimensional linear exhaustive search over the power splitting factor. Then, a suboptimal but low-complexity solution with closed-form expressions is proposed. The simulation results have shown that the proposed suboptimal solution achieves almost the same performance as that obtained by the exhaustive search. In addition, our proposed SR system with the full-duplex BD outperforms the half-duplex system with the time-division-multiplexing mode in general.

In this paper, we investigate and analyze a hybrid ambient backscatter (AmBack) scheme with harvest-then-transmit protocols. In this scheme, the whole transmission is divided into three phases (time slots): energy harvesting (EH), AmBack, and data transmission (DT). Depending on the type of the reflection coefficient (RC) for AmBack, two hybrid schemes are considered: variable RC (VRC) and fixed RC (FRC). In the VRC scheme, the RC changes with the channel state information, and the harvested energy in the first phase is totally used for DT. On the other hand, in the FRC scheme, the RC is fixed to be one, and the harvested energy in the first phase is in part used to cover the tag circuit operation in the second phase and in part for DT in the third phase. The resulting optimization problem is convex for the VRC scheme, but non-convex for the FRC scheme. However, by utilizing the maximum principle in convex optimization, we are able to derive closed-form expressions for optimal solutions for both schemes. In order to get more insights, we also analyze the upper bound performance of both schemes. Simulation results demonstrate that the AmBack/EH-AmBack scheme can always achieve the optimal performance.

In this paper, we investigate the robust power allocation issue in orthogonal frequency division multiplexing-based cognitive radio networks (CRNs) with unavoidable uncertainties (channel perturbations and variable environment). In this case, to control the performance degradation due to the uncertainties, we maximize the data rate of secondary users (SUs) by considering maximum allowable interference constraints and total power budget of SUs. To solve this problem, we design a controller for a switched affine system with state constraint. This system is based on a distributed projected dynamic system in accordance with the classical distributed convex optimization model for the power allocation and dynamic property of the Nash equilibrium. The robust controller design is on the basis of Lyapunov stability theory and linear matrix inequality to better realize the original power allocation from the control perspective. To the best of our knowledge, we are the first to solve the aforementioned problem by this kind of approach under the control frame, which is more practical for the realization in CRNs. Simulation results are provided to show the validation of the effectiveness of this approach in comparison with iterative water filling algorithm and the worst-case method.

The unknown noise variance and time-variant fading channels make the spectrum sensing design a challenging task for cognitive radios. Most existing sensing methods suffer from the information uncertainty and can hardly acquire promising performances in the adverse situations. To address this challenge, in this paper, we first formulate a dynamic state-space model for spectrum sensing, in which the unknown noise variance and time-variant flat fading channels are all taken into considerations. The dynamic behaviors of both primary user states and fading channels are characterized by two discrete state Markov chains. Based on this model, a novel spectrum sensing scheme is designed to recursively estimate the occupancy state of primary users, by estimating the time-variant fading channel gain and noise parameters jointly. The joint estimation is primarily premised on a maximum a posteriori probability criterion and the marginal particle filtering schemes. Simulation results are provided to demonstrate the advantages of our proposed method, which can significantly improve the sensing performance over time-variant flat fading channels, even with unknown noise variance.

In this work, an analytical framework for deriving the exact moments of multiple-input- multiple-output (MIMO) mutual information in the high-signal-to-noise ratio (SNR) regime is proposed. The idea is to make efficient use of the matrix-variate densities of channel matrices instead of the eigenvalue densities as in the literature. The framework is applied to the study of the emerging models of MIMO Rayleigh product channels and Jacobi MIMO channels, which include several well-known channels models as special cases. The corresponding exact moments of any order for the high-SNR mutual information are derived. The explicit moment expressions are utilized to construct simple yet accurate approximations to the outage probability. Despite the high-SNR nature, simulation shows usefulness of the approximations with finite SNR values in the scenario of low outage probability relevant to MIMO communications.

Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated.