Stochastic Sensor Scheduling via Distributed Convex Optimization

In this paper, we propose a stochastic scheduling strategy for estimating the states of N discrete-time linear time invariant (DTLTI) dynamic systems, where only one system can be observed by the sensor at each time instant due to practical resource constraints. The idea of our stochastic strategy is that a system is randomly selected for observation at each time instant according to a pre-assigned probability distribution. We aim to find the optimal pre-assigned probability in order to minimize the maximal estimate error covariance among dynamic systems. We first show that under mild conditions, the stochastic scheduling problem gives an upper bound on the performance of the optimal sensor selection problem, notoriously difficult to solve. We next relax the stochastic scheduling problem into a tractable suboptimal quasi-convex form. We then show that the new problem can be decomposed into coupled small convex optimization problems, and it can be solved in a distributed fashion. Finally, for scheduling implementation, we propose centralized and distributed deterministic scheduling strategies based on the optimal stochastic solution and provide simulation examples.Comment: Proof errors and typos are fixed. One section is removed from last versio

An Optimal UAV Deployment Algorithm for Bridging Communication

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In recent years, Unmanned Aerial Vehicles (UAVs) have attracted the attention of both the military and civilians because of their deployment in situations where part of the communication infrastructure is destroyed due to bomb blast, earthquake, flood, military operations or landslides. Also UAVs can be used in operations such as search and rescue, surveillance, forest fire monitoring, and border patrolling. Deployment of a UAV in a position where it can provide maximum coverage and high throughput is one of the vital problem that needs to be addressed. In this paper, we have proposed an optimal UAV deployment algorithm (OUDA) in order to bridge communication between two static nodes on the ground. In the proposed algorithm the UAV deploys to a position where it can provide the best communication facilities to both the nodes based on the received signal strength (RSS), and distance between nodes and UAV. Simulation results showed that the algorithm provides maximum throughput and low bit error rate (BER) once the UAV is fixed to an optimal position

Resource allocation and secure communication design in simultaneous wireless information and power transfer systems

Radio frequency (RF) energy transfer techniques have been regarded as the key enabling solutions to supply continuous and stable energy for the energy-constrained wireless devices. Simultaneous wireless information and power transfer (SWIPT) has been developed as a more promising RF energy transfer technique since it enables wireless information and wireless energy to access users from a same transmitted signal. Therefore, SWIPT has received remarkable attention. This thesis provides an investigation on applications and security issues of this emerging technology in various wireless communication scenarios. First, this thesis examines the application of SWIPT to a multi-user cooperative network in which the amplify-and-forward (AF) relay protocol is employed at the multi-antenna relay. A power splitting (PS) receiver architecture is utilized at each destination node to implement energy harvesting (EH) and information decoding (ID) simultaneously. The aim of this chapter is to minimize the relay transmit power by jointly designing relay beamforming vectors and PS ratios based on channel uncertainty models. The non-convex problem is converted into a semidefinite programming (SDP) problem by using the semidefinite relaxation (SDR) approach. In addition, a rank-one proof presents that the solution generated by the relaxed problem is optimal to the original problem. Second, a security issue about the SWIPT system is investigated in a cooperative network in the presence of potential eavesdroppers. The AF relay protocol and a PS receiver architecture are adopted at the multi-antenna relay and the desired destination node, respectively. Based on the system setup and the assumption of perfect channel state information (CSI), a transmit power minimization problem combined with the secrecy rate and harvested energy constraints is proposed to jointly optimize the beamforming vector and the PS ratio. The proposed optimization problem is non-convex and hard to tackle due to the issues of the quadratic terms and the coupled variables. To deal with this non-convex problem, two algorithms are proposed. In the first algorithm case, the proposed problem can be globally solved by using a two-level optimization approach which involves the SDR method and the one-dimensional (1-D) line search method. In addition, a rank reduction theorem is introduced to guarantee the tightness of the relaxation of the proposed scheme. In the second algorithm case, the proposed problem can be locally solved by exploiting a low complexity iterative algorithm which is embedded in the sequential parametric convex approximation (SPCA) method. Furthermore, the proposed optimization problem is extended to the imperfect CSI case. Third, a secure communication case is studied in an underlay multiple-input multiple-output (MIMO) cognitive radio (CR) network where the secondary transmitter (ST) provides SWIPT to receivers. In this chapter, two uncertainty channel models are proposed. One is based on the assumption that the ST has the perfect channel knowledge of the secondary information receiver (SIR) and the imperfect channel knowledge of secondary energy receivers (SERs) and primary receivers (PUs). The other one assumes that the ST only has the imperfect channel knowledge of all receivers. In each uncertainty channel model, an outage-constrained secrecy rate maximization (OC-SRM) problem combined with probability constraints is proposed to jointly optimizing the transmit covariance matrix and the artificial noise (AN)- aided covariance matrix. The designed OC-SRM problem for both models is non-convex due to the unsolvable probabilistic constraints. To solve this non-convex problem, the log determinant functions are first approximated to the easy handle the functions that the channel error terms are included in the trace function. Then, the probability constraints are converted into the deterministic constraints by exploiting the Bernstein-type inequality (BTI) approach. Finally, the reformulated problem for both models is solvable by using the existing convex tools. Last, a novel security issue is investigated in a MIMO-SWIPT downlink network where nonlinear energy receivers (ERs) are considered as the potential eavesdroppers. In this chapter, two uncertainty channel models, namely partial channel uncertainty (PCU) and full channel uncertainty (FCU), are proposed. An OC-SRM problem of each model is proposed to design the transmit signal covariance matrix while satisfying probabilistic constraints of the secrecy rate and the harvested energy. To surmount the non-convexity of the proposed OC-SRM problem in each model, several transformations and approximations are utilized. In the PCU model, the OC-SRM problem is first converted into two subproblems by introducing auxiliary variables. Then, three conservative approaches are adopted to obtain the safe approximation expressions of the probabilistic constraints, which are deterministic constraints. Moreover, an alternating optimization (AO) algorithm is proposed to iteratively solve two convex conic subproblems. In the FCU model, log determinant functions are first approximated to the trace functions. Then, the three approaches aforementioned are employed to convert probabilistic constraints into deterministic ones. The bisection method is utilized to solve the reformulated problem. Finally, the computational complexity of the proposed three approaches based on the PCU and FCU model is analyzed

Power Allocation for Conventional and Buffer-Aided Link Adaptive Relaying Systems with Energy Harvesting Nodes

Energy harvesting (EH) nodes can play an important role in cooperative communication systems which do not have a continuous power supply. In this paper, we consider the optimization of conventional and buffer-aided link adaptive EH relaying systems, where an EH source communicates with the destination via an EH decode-and-forward relay. In conventional relaying, source and relay transmit signals in consecutive time slots whereas in buffer-aided link adaptive relaying, the state of the source-relay and relay-destination channels determines whether the source or the relay is selected for transmission. Our objective is to maximize the system throughput over a finite number of transmission time slots for both relaying protocols. In case of conventional relaying, we propose an offline and several online joint source and relay transmit power allocation schemes. For offline power allocation, we formulate an optimization problem which can be solved optimally. For the online case, we propose a dynamic programming (DP) approach to compute the optimal online transmit power. To alleviate the complexity inherent to DP, we also propose several suboptimal online power allocation schemes. For buffer-aided link adaptive relaying, we show that the joint offline optimization of the source and relay transmit powers along with the link selection results in a mixed integer non-linear program which we solve optimally using the spatial branch-and-bound method. We also propose an efficient online power allocation scheme and a naive online power allocation scheme for buffer-aided link adaptive relaying. Our results show that link adaptive relaying provides performance improvement over conventional relaying at the expense of a higher computational complexity.Comment: Submitted to IEEE Transactions on Wireless Communication