Energy efficient resource allocation for future wireless communication systemsy

Next generation of wireless communication systems envisions a massive number of connected battery powered wireless devices. Replacing the battery of such devices is expensive, costly, or infeasible. To this end, energy harvesting (EH) is a promising technique to prolong the lifetime of such devices. Because of randomness in amount and availability of the harvested energy, existing communication techniques require revisions to address the issues specific to EH systems. In this thesis, we aim at revisiting fundamental wireless communication problems and addressing the future perspective on service based applications with the specific characteristics of the EH in mind. In the first part of the thesis, we address three fundamental problems that exist in the wireless communication systems, namely; multiple access strategy, overcoming the wireless channel, and providing reliability. Since the wireless channel is a shared medium, concurrent transmissions of multiple devices cause interference which results in collision and eventual loss of the transmitted data. Multiple access protocols aim at providing a coordination mechanism between multiple transmissions so as to enable a collision free medium. We revisit the random access protocol for its distributed and low energy characteristics while incorporating the statistical correlation of the EH processes across two transmitters. We design a simple threshold based policy which only allows transmission if the battery state is above a certain threshold. By optimizing the threshold values, we show that by carefully addressing the correlation information, the randomness can be turned into an opportunity in some cases providing optimal coordination between transmitters without any collisions. Upon accessing the channel, a wireless transmitter is faced with a transmission medium that exhibits random and time varying properties. A transmitter can adapt its transmission strategy to the specific state of the channel for an efficient transmission of information. This requires a process known as channel sensing to acquire the channel state which is costly in terms of time and energy. The contribution of the channel sensing operation to the energy consumption in EH wireless transmitters is not negligible and requires proper optimization. We developed an intelligent channel sensing strategy for an EH transmitter communicating over a time-correlated wireless channel. Our results demonstrate that, despite the associated time and energy cost, sensing the channel intelligently to track the channel state improves the achievable long-term throughput significantly as compared to the performance of those protocols lacking this ability as well as the one that always senses the channel. Next, we study an EH receiver employing Hybrid Automatic Repeat reQuest (HARQ) to ensure reliable end-to-end communications. In inherently error-prone wireless communications systems, re-transmissions triggered by decoding errors have a major impact on the energy consumption of wireless devices. We take into account the energy consumption induced by HARQ to develop simple-toimplement optimal algorithms that minimizes the number of retransmissions required to successfully decode the packet. The large number of connected edge devices envisioned in future wireless technologies enable a wide range of resources with significant sensing capabilities. The ability to collect various data from the sensors has enabled many exciting smart applications. Providing data at a certain quality greatly improves the performance of many of such applications. However, providing high quality is demanding for energy limited sensors. Thus, in the second part of the thesis, we optimize the sensing resolution of an EH wireless sensor in order to efficiently utilize the harvested energy to maximize an application dependent utilit

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Power Management Strategies in Energy-Harvesting Wireless Sensor Networks

Power management strategies are extremely important in Wireless Sensor Networks (WSNs). The objective is to make the nodes operate as long as possible. In the same context, in this article, our aim is to provide the optimal transmission power to maximize the network lifetime using the Orthogonal Multiple Access Channel (OMAC) in Harvesting System (HS). We consider that the nodes have direct communication with a Fusion Center (FC) with causal Channel Side Information (CSI) at the sender and receiver.We begin the analysis by considering a single transmitter node powered by a rechargeable battery with limited capacity energy. Afterward, we generalize the analysis with M transmitter nodes. In both cases, the transmitters are able to harvest energy from nature.Eventually, we show the viability of our approach in simulations results

Age-Energy Tradeoff in Fading Channels with Packet-Based Transmissions

The optimal transmission strategy to minimize the weighted combination of age of information (AoI) and total energy consumption is studied in this paper. It is assumed that the status update information is obtained and transmitted at fixed rate over a Rayleigh fading channel in a packet-based wireless communication system. A maximum transmission round on each packet is enforced to guarantee certain reliability of the update packets. Given fixed average transmission power, the age-energy tradeoff can be formulated as a constrained Markov decision process (CMDP) problem considering the sensing power consumption as well. Employing the Lagrangian relaxation, the CMDP problem is transformed into a Markov decision process (MDP) problem. An algorithm is proposed to obtain the optimal power allocation policy. Through simulation results, it is shown that both age and energy efficiency can be improved by the proposed optimal policy compared with two benchmark schemes. Also, age can be effectively reduced at the expense of higher energy cost, and more emphasis on energy consumption leads to higher average age at the same energy efficiency. Overall, the tradeoff between average age and energy efficiency is identified

Secure Communication with a Wireless-Powered Friendly Jammer

In this paper, we propose to use a wireless-powered friendly jammer to enable secure communication between a source node and destination node, in the presence of an eavesdropper. We consider a two-phase communication protocol with fixed-rate transmission. In the first phase, wireless power transfer is conducted from the source to the jammer. In the second phase, the source transmits the information-bearing signal under the protection of a jamming signal sent by the jammer using the harvested energy in the first phase. We analytically characterize the long-time behavior of the proposed protocol and derive a closed-form expression for the throughput. We further optimize the rate parameters for maximizing the throughput subject to a secrecy outage probability constraint. Our analytical results show that the throughput performance differs significantly between the single-antenna jammer case and the multi-antenna jammer case. For instance, as the source transmit power increases, the throughput quickly reaches an upper bound with single-antenna jammer, while the throughput grows unbounded with multi-antenna jammer. Our numerical results also validate the derived analytical results.Comment: accepted for publication in IEEE Transactions on Wireless Communication

Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network