Channel Selection Policy Research Articles

Jamming and Eavesdropping Defense Scheme Based on Deep Reinforcement Learning in Autonomous Vehicle Networks

As a legacy from conventional wireless services, illegal eavesdropping is regarded as one of the critical security challenges in Connected and Autonomous Vehicles (CAVs) network. Our work considers the use of Distributed Kalman Filtering (DKF) and Deep Reinforcement Learning (DRL) techniques to improve anti-eavesdropping communication capacity and mitigate jamming interference. Aiming to improve the security performance against smart eavesdropper and jammer, we first develop a DKF algorithm that is capable of tracking the attacker more accurately by sharing state estimates among adjacent nodes. Then, a design problem for controlling transmission power and selecting communication channel is established while ensuring communication quality requirements of the authorized vehicular user. Since the eavesdropping and jamming model is uncertain and dynamic, a hierarchical Deep Q-Network (DQN)-based architecture is developed to design the anti-eavesdropping power control and possibly channel selection policy. Specifically, the optimal power control scheme without prior information of the eavesdropping behavior can be quickly achieved first. Based on the system secrecy rate assessment, the channel selection process is then performed when necessary. Simulation results confirm that our jamming and eavesdropping defense technique enhances the secrecy rate as well as achievable communication rate compared with currently available techniques.

On Communication Performance in Energy Harvesting WSNs Under a Cooperative Jamming Attack

In this article, we consider the system performance of an energy harvesting (EH) wireless sensor network in terms of reliable communications when subjected to a cooperative jamming attack. A set of strategically located nodes acting as cluster heads (CHs) transfer energy to the wireless sensors within range, forming a cluster. The sensors use this energy to transmit data to the CHs, which, in turn, deliver the information to a base station (BS) using nonorthogonal multiple access. The BS processes the collected information and synchronizes the operation of all CHs. Furthermore, there exist two adversaries, namely, a jamming attacker and an eavesdropper, who cooperate to attack the considered system. To protect against this attack, the CHs should be controlled by suitable power allocation coefficients obtained from the security constraints of the CHs. Using these constraints, closed-form expressions are derived to find the power allocation coefficients that will enable reliable and secure communication. In addition, we propose an interference channel selection policy for the sensor-to-CHs links and CHs-to-BS links to improve the reliability of communication while enhancing energy utilization. Finally, an algorithm for finding the optimal EH time is also proposed.

Probabilistic Selective Broadcast for energy-saving communications in Cognitive Ad Hoc Networks

Cognitive radio, as a promising technology, plays an important role in ad hoc network communications, leading to more reasonable and orderly utilizations of wireless spectrum resources. It is not only an opportunity but also a challenge for wireless mobile communications. In cognitive radio ad hoc networks, the non-uniform channel arrays available for secondary users make difficult to the application of broadcasting technologies, concentrating on common channel selection and broadcast collision. In this paper, a probabilistic selective broadcast, called pSB, is proposed, aiming at economy and energy-saving distributed wireless communications. The protocol adopts an intelligent channel selection policy with a probabilistic forwarding strategy to alleviate collisions caused by redundant message exchanges. Consequently, the proposed pSB outperforms other methods in terms of higher message reach rates and fewer broadcast collisions evaluated by simulations under different parameter configurations.

Hybrid Directional CR-MAC based on Q-Learning with Directional Power Control

In this paper, we investigate the Hybrid Directional CR-MAC based on Q-Learning with Directional Power Control in cognitive radio (CR) systems. In CR systems, nodes can switch to heterogeneous non-overlapping channels opportunistically which offer higher achievable throughput. However, the random channel selection policy in existing CR-MAC protocol has problems like delay, packet collisions, and quality of service. The proposed channel selection scheme which is quite different from the traditional scheme is adopted by nodes to achieve context awareness and intelligence for adaptive channel selection. The nodes select a channel based on the results learned by interactions with the other nodes and channels. The directional transmission power control scheme allows the nodes to reuse the channels subject to interference constraints. The simulation results show that nodes using the proposed algorithm can select channels adaptively and optimal transmission power which helps to achieve high throughput and minimized power consumption.

A Multichannel Medium Access Control Protocol for Vehicular Power Line Communication Systems

In-vehicle communications are emerging to play an important role in the continued development of reliable and efficient X-by-Wire applications in new vehicles. Since vehicle devices, sensors, and the electronic control unit (ECU) are already connected to power wires, the advancement of power line communications (PLCs) can provide a very low cost and virtually free platform for in-vehicle communications. In this paper, we propose a medium access control (MAC) protocol for vehicular PLC systems, where multiple nodes are competing for transmission over the direct current (dc) power line. The proposed protocol uses a combination of time and frequency multiplexing and consists of two key features: 1) a distributed channel selection policy to arbitrate packet transmission across different channels and provide robustness against interference and noise and 2) a distributed collision resolution algorithm to allow efficient nodes completion over selected channels. Specifically, the collision resolution algorithm is optimized with respect to the channel policy such that the success probability of transmission in each channel is maximized. Numerical results are also supplemented to validate the performance of the proposed protocol and provide useful guidelines for developing a robust contention-based MAC protocol for vehicular PLC systems.

Channel Selection Policy in Multi-SU and Multi-PU Cognitive Radio Networks with Energy Harvesting for Internet of Everything

Cognitive radio, which will become a fundamental part of theInternet of Everything(IoE), has been identified as a promising solution for the spectrum scarcity. In a multi-SU and multi-PU cognitive radio network, selecting channels is a fundamental problem due to the channel competition among secondary users (SUs) and packet collision between SUs and primary users (PUs). In this paper, we adopt cooperative sensing method to avoid the packet collision between SUs and PUs and focus on how to collect the spectrum sensing data of SUs for cooperative sensing. In order to reduce the channel competition among SUs, we first consider thehybridtransmission model for single SU where a SU can opportunistically access both idle channels operating either theOverlayor theUnderlaymodel and the busy channels by using the energy harvesting technology. Then we propose a competitive set based channel selection policy for multi-SU where all SUs competing for data transmission or energy harvesting in the same channel will form a competitive set. Extensive simulations show that the proposed cooperative sensing method and the channel selection policy outperform previous solutions in terms of false alarm, average throughput, average waiting time, and energy harvesting efficiency of SUs.

Channel Selection in Multichannel Cognitive Radio Systems Employing RF Energy Harvesting

A decentralized channel access strategy is proposed for a multichannel low-power cognitive radio (CR) system. CRs are equipped with rechargeable batteries to harvest radio-frequency (RF) energy from the primary user (PU) transmission. At any time, secondary users (SUs) can exploit either the spatiotemporal spectrum opportunities for transmission or the PU active periods for RF energy harvesting. An optimal channel-selection probability vector is obtained to achieve the maximum SU throughput by finding sufficient spectrum-access and energy-harvesting opportunities while accounting for SU contention. The authors have a nonconvex optimization problem. It is decomposed to obtain a convex optimization subproblem, but an exhaustive search is required for the master problem. Alternatively, an iterative algorithm is proposed wherein the authors define a combined metric to consider the spectrum-access and energy-harvesting opportunities on different PU channels. A convex optimization problem is solved with a closed-form solution in each iteration. Numerical results show that the proposed channel-selection policy enables an SU to achieve better expected throughput through an enhanced battery-recharging probability.

Dynamic spectrum access in cognitive radio networks with RF energy harvesting

Spectrum efficiency and energy efficiency are two critical issues in designing wireless networks. Through dynamic spectrum access, cognitive radios can improve the spectrum efficiency and capacity of wireless networks. On the other hand radio frequency (RF) energy harvesting has emerged as a promising technique to supply energy to wireless networks and thereby increase their energy efficiency. Therefore, to achieve both spectrum and energy efficiencies, the secondary users in a cognitive radio network (CRN) can be equipped with the RF energy harvesting capability, and such a network can be referred to as an RF-powered cognitive radio network. In this article we provide an overview of the RF-powered CRNs and discuss the challenges that arise for dynamic spectrum access in these networks. Focusing on the trade-off among spectrum sensing, data transmission, and RF energy harvesting, we then discuss the dynamic channel selection problem in a multi-channel RF-powered CRN. In the RF-powered CRN a secondary user can adaptively select a channel to transmit data when the channel is not occupied by any primary user. Alternatively, the secondary user can harvest RF energy for data transmission if the channel is occupied. The optimal channel selection policy of the secondary user can be obtained by formulating a Markov decision process (MDP) problem. We present some numerical results obtained by solving this MDP problem.

Distributed Exploitation of Spectrum and Channel State Information for Channel Reservation and Selection in Interweave Cognitive Radio Networks

A channel-and-sensing-aware channel access (CSCA) policy is proposed for multi-channel interweave cognitive radio systems, where multiple secondary users (SUs) are competing for transmission to a common access point. The proposed CSCA policy consists of two key elements: (i) a decentralized channel selection policy that allows each SU to utilize knowledge of both the spectrum occupancy information and its local channel state information to make local channel access decisions and (ii) a channel reservation policy that allows each SU to compete for use of its selected channel by emitting short reservation packets at the beginning of each frame. In the reservation period, a channel-aware splitting algorithm is utilized to resolve collision among SUs that are competing for the same channel. The splitting procedure is optimized with respect to SUs' channel selection policy and ensures that the SU with the best channel quality prevails when collision is resolved. The CSCA policy is derived with the goal of maximizing the SU's throughput subject to a constraint on the probability of collision with primary users (PUs). To satisfy the collision probability constraint, a minimum channel gain threshold is set on each channel to limit the probability that the channel is accessed by SUs. An iterative algorithm is proposed to optimize the parameters in the channel selection and reservation policies, and a low-complexity policy is devised for use in systems with large numbers of channels. The proposed CSCA policies allow SUs to exploit optimally the tradeoff between spectrum availability and channel quality.

Algorithms for Dynamic Spectrum Access With Learning for Cognitive Radio

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm. </para>

Optimality of Myopic Sensing in Multichannel Opportunistic Access

This paper considers opportunistic communication over multiple channels where the state (ldquogoodrdquo or ldquobadrdquo) of each channel evolves as independent and identically distributed (i.i.d.) Markov processes. A user, with limited channel sensing capability, chooses one channel to sense and decides whether to use the channel (based on the sensing result) in each time slot. A reward is obtained whenever the user senses and accesses a ldquogoodrdquo channel. The objective is to design a channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast as a partially observed Markov decision process (POMDP) or a restless multiarmed bandit process, to which optimal solutions are often intractable. This paper shows that a myopic policy that maximizes the immediate one-step reward is optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to two or three, while presenting a counterexample for the case of four channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and antijamming.

Distributed wireless channel allocation in networks with mobile base stations

In traditional cellular networks with fixed base stations, the channel reuse pattern is static and deterministic. When the cell layout is dynamic, due to the mobility of base stations, the cluster of cells within cochannel interference range changes with time. Consequently, the channel reuse pattern is highly dynamic. Moreover, base stations also need wireless channels to communicate amongst themselves. A communication session between a pair of nodes may have to switch channels due to the movement of other nodes into the neighborhood. None of the existing dynamic channel allocation algorithms for cellular networks works in such a system. Hence, there is a need for new wireless channel allocation algorithms for virtual cellular networks with mobile base stations. In this paper, principles of mutual exclusion pertaining to distributed computing systems are employed to develop such an algorithm. The inter-base-station wireless links are referred to as backbone links, while the base station to mobile node links are referred to as short-hop links. The proposed algorithm is distributed, dynamic, and deadlock-free. Disjoint sets of channels are used for backbone and short-hop links. The distributed nature of the channel allocation scheme leads to scalability and robustness, as the responsibility is no longer centralized at the mobile telecommunications switching office (MTSO). Instead, it is shared among all the mobile base stations. In addition, the issue of channel rearrangement is addressed. Channel rearrangement is the switching of channels, performed to prevent cochannel interference, when mobile base stations using the same channels, hitherto not in interference range, come within the range of each other. If multiple channels are available to support a communication session, the channel selection policy can have a significant impact on performance. A random selection from the set of available channels yields better performance at low to moderate channel demand. This is in contrast to ordered channel selection (from one end of the spectrum), which always yields the best performance for cellular networks. Results obtained by simulating the algorithm are consistent with the theoretically obtained values.

Transmission algorithms for a multi‐channel packet radio system with priority users

AbstractIn multi‐channel systems such as multi‐station packet radio networks or cellular radio, various user categories arise. In this paper, we propose that one portion of the users, named low‐priority, each be assigned a specific channel for their transmissions, whereas another portion, named high‐priority, may transmit through any of the channels via some channel selection policy. The main objective is to reduce delays of the high‐priority users without imposing heavy penalties on the delays of the low‐priority group. For the described system, we adopt a previously studied limited sensing random access algorithm and introduce two dynamic channel selection policies. We study the stability region and the delays induced by the system. The selection policies present the high‐priority users with a significant delay advantage and induce fair utilization of the transmission channels, without requiring any knowledge about the system state by the users. One of the selection policies induces high system throughput at the expense of implementation complexity and increased delays for the low‐priority users. The second policy is easier to implement but, although it does not penalize the delays of the low‐priority users, it reduces the system thoughput.