차세대 무선 통신 시스템을 위한 에너지 하비스팅 및 보안 요소가 고려된 무선 자원 관리 기 연구

Abstract

In recent years, the available spectrum becomes more and more scarce and deserves utilization efficiency to avoid bottlenecks in surging wireless traffic demand. With this regard, spectrum reuse is required to mitigate interference when using wireless communications. As promising solutions to the spectrum scarcity problems, Cognitive radio (CR) technology is a communication paradigm that allows non-licensed users (i.e. cognitive users) to opportunistically access spectrum holes that are temporally unoccupied by licensed users (i.e. primary users) at a particular time and geographic location. Therefore, wireless networks can be greened by the CR technique that is capable of not only dealing with spectrum scarcity but also improving the energy deficiency of wireless users. In addition, energy harvesting (EH) in cognitive radio networks (CRNs) has been applied and considered as promising topics for many researchers. Although harvesting ability has been limited and still needs to be improved, EH-powered CRNs have been widely investigated in many aspects such as relay selection, transmission power allocation, and packet duration optimization.

Intuitively, limited energy harvesting capability on wireless communications is seen as one of the crucial issues in designing energy-efficient resource assignment approaches. Moreover, similar to traditional wireless networks, CRNs also face vulnerabilities regarding information security such as malicious attacking, jamming, or eavesdropping, which would be more challenging in future resource allocation. Nowadays, with the assistance of artificial intelligent (AI) paradigms such as machine learning, game theory, and meta-heuristics, the wireless networks get intelligent in the practical deployment. Among them, POMDP and reinforcement learning approaches are well-known for their useful applications in resource allocation optimization. Therefore, it is vital to employ these innovative methods to improve the quality of services in long-term and maintenance-free operation of the energy harvesting-powered wireless networks. Motivated by the foregoing analysis, this dissertation focuses on studying the robust resource allocation solution (e.g. transmission energy, frequency bands) to maximize the long-term performance of the EH-powered CRNs with and without information of harvested energy distribution. Furthermore, by leveraging the advantage of the CR technique, the hybrid scheduling method using both CR channels and ISM channels is investigated to enhance successful packet delivery ratio in industrial wireless networks with the consideration of ISM channels' interference. The performance of the proposed methods is validated through numerical simulation under the numerous network parameters. Specifically, this dissertation will address the current challenges in wireless networks as follows:

Firstly, we consider jamming attacks in the physical layer of multi-hop cognitive radio networks (MHCRNs) where energy-constrained relays forward information from the source to the destination. Meanwhile, a jammer can transmit interfering signals on a channel such that all ongoing transmissions on this channel will be corrupted. All jammers can attack only one of the predefined channels in each time slot and can randomly switch channels to start jamming another channel at the beginning of every time slot. The switching behavior is assumed to follow a Poisson distribution. Energy harvesting is utilized in the network such that relays are able to harvest energy from non-radio frequency (non-RF) signals such as solar, wind, or temperature. We determine the throughput/delay ratio as a key metric to evaluate the performance in MHCRNs. Owing to the limited battery capacity in the relays and the jamming problem, the source needs to select proper relays and channels for each data transmission frame to optimize overall network performance in terms of end-to-end delay, throughput, and energy efficiency. Therefore, we provide two novel multihop allocation schemes to maximize achievable end-to-end throughput while minimizing delay in the presence of jammers.

Secondly, we investigate an attack strategy for a legitimate energy-constrained eavesdropper (e.g., a government agency) to efficiently capture the suspicious wireless communications (e.g., an adversary communications link) in the physical layer of a CRN in tactical wireless networks. Since it is powered by an energy harvesting device, a full-duplex active eavesdropper constrained by a limited energy budget can simultaneously capture data and interfere with the suspicious cognitive transmissions to maximize the achievable wiretap rate while minimizing the suspicious transmission rate over a Rayleigh fading channel. The cognitive user operation is modeled in a time-slotted fashion. We formulate the problem of maximizing a legitimate attack performance by adopting the framework of a partially observable Markov decision process. The decision is determined based on the remaining energy and a belief regarding the licensed channel activity in each time slot. Particularly, in each time slot, the eavesdropper can perform an optimal action based on two functional modes: (1) passive eavesdropping (overhearing data without jamming) or (2) active eavesdropping (overhearing data with the optimal amount of jamming energy) to maximize the long-term benefit.

Thirdly, we consider a centralized multi-channel cognitive radio network in the presence of eavesdroppers (EVEs). In the network, the secondary base station (SBS) shares currently free primary channels to simultaneously communicate with secondary users (SUs), while passive eavesdroppers attempt to overhear data in the secondary communications. Each limited-battery SU is equipped with two antennas (one for transmitting signals, and other for receiving signals) and is powered by a solar energy harvester. Meanwhile, the SBS equipped with multiple antennas can operate in full-duplex (FD) transmission mode (simultaneously transmit and receive signals) or in half-duplex (HD) transmission mode (transmit and receive signals in turn during each half of a time slot) with the SUs. We propose a novel scheme to maximize the secondary system security of the multi-channel cognitive system in the presence of multiple passive EVEs, in which the EVEs are able to overhear the data of the SBS-SU transmissions on all the primary channels. The problem of decision making is formulated as the framework of a partially observable Markov decision process (POMDP), and an optimal solution is achieved by adopting value iteration-based dynamic programming. Specifically, in each time slot, the SBS allocates optimal channel and optimal action (i.e. either stay silent or employ HD/FD transmission modes with optimal transmission power) for each SU in order to obtain maximum long-term secrecy rate for the secondary system.

Next, we consider a system of caching-based UAV-assisted communications between multiple ground users (GUs) and a local station (LS). Specifically, a UAV is exploited to cache data from the LS and then serve GUs’ requests to handle the issue of unavailable or damaged links from the LS to the GUs. The UAV can harvest solar energy for its operation. We investigate joint cache scheduling and power allocation schemes by using non-orthogonal multiple access (NOMA) technique to maximize the long-term down-link rate. Two scenarios for the network are taken into account. In the first, the harvested energy distribution of the GUs is assumed to be known, we propose a partially observable Markov decision process framework such that the UAV can allocate optimal transmission power for each GU based on proper content caching over each flight period. In the second scenario where the UAV does not know the environment’s dynamics in advance, an actor-critic-based scheme is proposed to achieve a solution by learning with a dynamic environment.

Then, we study the optimal scheme of maximizing the packet delivery ratio in industrial wireless systems. To enhance the transmission performance of the WirelessHART network, the cognitive radio (CR) technique is applied such that joint CR/Industrial Scientific Medical (ISM) channels are scheduled for data transmissions of the field devices. Each CR-enabled device has a limited buffer capacity, and the cognitive channels' behavior is modeled as the discrete Markov chain. The packets generated at each device are routed to the gateway (GW) through the aid of neighbor devices. Access Points (APs) are deployed to improve the successful transmission probability of the packets by using cognitive radio technology. Moreover, the APs can harvest solar energy from the sunlight environment. The problem of long-term throughput maximization is formulated as a framework of a Markov decision process. Subsequently, we propose the deep reinforcement learning-based scheme to optimally assign multiple ISM and cognitive radio channels to the field devices to maximize the received packets at the gateway.

Finally, we summarize the main contributions of this dissertation and discuss future research directions for the next-generation wireless networks.