Sensing Throughput Optimization in Fading Cognitive Multiple Access Channels With Energy Harvesting Secondary Transmitters

The paper investigates the problem of maximizing expected sum throughput in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of sum-capacity of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer non-linear program with respect to two decision variables namely spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU \textit{transmission power} and \textit{sensing time}, which is non-convex in nature. We solve the resulting optimization problem as an alternating convex optimization problem for both non-causal and causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC). We present an analytic solution for the non-causal scenario with infinite battery capacity for a general finite horizon problem.We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms

Quantized Non-Bayesian Quickest Change Detection with Energy Harvesting

This paper focuses on the analysis of an optimal sensing and quantization strategy in a multi-sensor network where each individual sensor sends its quantized log-likelihood information to the fusion center (FC) for non-Bayesian quickest change detection. It is assumed that the sensors are equipped with a battery/energy storage device of finite capacity, capable of harvesting energy from the environment. The FC is assumed to have access to either non-causal or causal channel state information (CSI) and energy state information (ESI) from all the sensors while performing the quickest change detection. The primary observations are assumed to be generated from a sequence of random variables whose probability distribution function changes at an unknown time point. The objective of the detection problem is to minimize the average detection delay of the change point with respect to a lower bound on the rate of false alarm. In this framework, the optimal sensing decision and number of quantization bits for information transmission can be determined with the constraint of limited available energy due to finite battery capacity. This optimization is formulated as a stochastic control problem and is solved using dynamic programming algorithms for both non-causal and causal CSI and ESI scenario. A set of non-linear equations is also derived to determine the optimal quantization thresholds for the sensor log-likelihood ratios, by maximizing an appropriate Kullback-Leibler (KL) divergence measure between the distributions before and after the change. A uniform threshold quantization strategy is also proposed as a simple sub-optimal policy. The simulation results indicate that the optimal quantization is preferable when the number of quantization bits is low as its performance is significantly better compared to its uniform counterpart in terms of average detection delay. For the case of a large number of quantization bits, the performance benefits of using the optimal quantization as compared to its uniform counterpart diminish, as expected

<em>Ocimum</em> Phytochemicals and Their Potential Impact on Human Health

The genus Ocimum (Lamiaceae) is distributed all over the world and can be found in many environments. Ocimum species is a rich source of various phytochemicals including tannins, phenolic acids, anthocyanins, phytosterols, and policosanols. These phytochemicals have the potential to significantly impact human health. The economic importance of Ocimum is also evident; Ocimum oil and its constituents and derivatives are used as flavoring agents throughout the world in food, pharmaceutical, herbal, perfumery, and flavoring industry. The important advantages of Ocimum plants in various treatments are their safety besides being less expensive, efficacy and availability throughout the world. This paper will focus on the biological effects of Ocimum essential oils, with particular attention on the molecular mechanism underlying their action

Distributed Detection and Its Applications with Energy Harvesting Wireless Networks

With the advent and widespread applications of high data-rate wireless services and devices, two of the fundamental resources in wireless communication have become extremely important and are scarce. These two resources are bandwidth and energy respectively. To tackle the problem of the ever-growing requirements of bandwidth, the paradigm of cognitive radio has been proposed in the literature where the users without any license are capable of utilizing the wireless radio spectrum allocated to the licensed user when it is idle. The performance of such a dynamic spectrum allocation policy depends heavily on the unlicensed users' ability to detect the vacancy in a licensed user's radio spectrum. Different types of detection algorithms have been investigated in the literature for this purpose. Some classic detection techniques like energy detection, matched filter detection, cyclo-stationary detector, generalized likelihood ratio test detector have found significant applications in sensing the licensed spectrum. These group of detection techniques focuses on collecting samples and performing the detection in a non-sequential fashion. The sequential counterpart of such techniques which are implemented sequentially at every time instant has also been studied extensively. Tackling the evergrowing energy requirement has been the other major challenge for wireless communication. To address this issue, a significant amount of research has been dedicated to the idea of incorporating the capability of energy harvesting in wireless devices. Further research in this domain has also introduced the idea of wireless energy sharing, where individual users are additionally capable of sharing energy with each other. The problem with such systems is the inherently stochastic nature of the energy harvesting process. Furthermore, there are practical limitations of the size of the battery for each user, which limits the amount of energy that can be stored at a particular time instant. Motivated by these two factors, the work presented in this thesis has its focus on cognitive radio networks with energy harvesting capability. In the aforementioned network, unlicensed users are concerned with achieving two fundamental goals. Firstly, they want to efficiently utilize the radio spectrum when the licensed user is not active, which results in the sum-throughput maximization problem with energy harvesting constraint. We have also investigated this problem where individual unlicensed users are capable of sharing energy with each other. Secondly, they want to detect the change in the activity in the licensed user spectrum as soon as possible. Motivated by this goal, we have investigated the problem of change point detection delay minimization in wireless sensor networks with energy harvesting constraints in a decentralized setting. Furthermore, we have explored the detection delay parameter for the decentralized settings with local decisions with similar constraints of energy availability due to energy harvesting

Distributed Detection and Its Applications with Energy Harvesting Wireless Networks

With the advent and widespread applications of high data-rate wireless services and devices, two of the fundamental resources in wireless communication have become extremely important and are scarce. These two resources are bandwidth and energy respectively. To tackle the problem of the ever-growing requirements of bandwidth, the paradigm of cognitive radio has been proposed in the literature where the users without any license are capable of utilizing the wireless radio spectrum allocated to the licensed user when it is idle. The performance of such a dynamic spectrum allocation policy depends heavily on the unlicensed users' ability to detect the vacancy in a licensed user's radio spectrum. Different types of detection algorithms have been investigated in the literature for this purpose. Some classic detection techniques like energy detection, matched filter detection, cyclo-stationary detector, generalized likelihood ratio test detector have found significant applications in sensing the licensed spectrum. These group of detection techniques focuses on collecting samples and performing the detection in a non-sequential fashion. The sequential counterpart of such techniques which are implemented sequentially at every time instant has also been studied extensively. Tackling the evergrowing energy requirement has been the other major challenge for wireless communication. To address this issue, a significant amount of research has been dedicated to the idea of incorporating the capability of energy harvesting in wireless devices. Further research in this domain has also introduced the idea of wireless energy sharing, where individual users are additionally capable of sharing energy with each other. The problem with such systems is the inherently stochastic nature of the energy harvesting process. Furthermore, there are practical limitations of the size of the battery for each user, which limits the amount of energy that can be stored at a particular time instant. Motivated by these two factors, the work presented in this thesis has its focus on cognitive radio networks with energy harvesting capability. In the aforementioned network, unlicensed users are concerned with achieving two fundamental goals. Firstly, they want to efficiently utilize the radio spectrum when the licensed user is not active, which results in the sum-throughput maximization problem with energy harvesting constraint. We have also investigated this problem where individual unlicensed users are capable of sharing energy with each other. Secondly, they want to detect the change in the activity in the licensed user spectrum as soon as possible. Motivated by this goal, we have investigated the problem of change point detection delay minimization in wireless sensor networks with energy harvesting constraints in a decentralized setting. Furthermore, we have explored the detection delay parameter for the decentralized settings with local decisions with similar constraints of energy availability due to energy harvesting

Sum Throughput Maximization in a Cognitive Multiple Access Channel with Cooperative Spectrum Sensing and Energy Harvesting

This paper focuses on the problem of sensing throughput optimization in a fading multiple access cognitive radio (CR) network, where the secondary user (SU) transmitters participate in cooperative spectrum sensing and are capable of harvesting energy and sharing energy with each other. We formulate the optimization problem as a maximization of the expected achievable sum-rate over a finite horizon, subject to an average interference constraint at the primary receiver, peak power constraints, and energy causality constraints at the SU transmitters. The optimization problem is a non-convex, mixed integer non-linear program (MINLP) involving the binary action to sense the spectrum or not, and the continuous variables, such as the transmission power, shared energy, and sensing time. The problem is analyzed under two different assumptions on the available information pattern: 1) non-causal channel state information (CSI), energy state information (ESI), and infinite battery capacity and 2) the more realistic scenario of the causal CSI/ESI and finite battery. In the non-casual case, this problem can be solved by an exhaustive search over the decision variable or an MINLP solver for smaller problem dimensions, and a novel heuristic policy for larger problems, combined with an iterative alternative optimization method for the continuous variables. The causal case with finite battery is optimally solved using a dynamic programming (DP) methodology, whereas a number of sub-optimal algorithms are proposed to reduce the computational complexity of DP. Extensive numerical simulations are carried out to illustrate the performance of the proposed algorithms. One of the main findings indicates that the energy sharing is more beneficial when there is a significant asymmetry between average harvested energy levels/channel gains of different SUs

On Optimal Quantized Non-Bayesian Quickest Change Detection with Energy Harvesting

In this paper, we consider a problem of decentralized non-Bayesian quickest change detection using a wireless sensor network where the sensor nodes are powered by harvested energy from the environment. The underlying random process being monitored by the sensors is subject to change in its distribution at an unknown but deterministic time point, and the sensors take samples (sensing) periodically, compute the likelihood ratio based on the distributions before and after the change, quantize it and send it to a remote fusion centre (FC) over fading channels for performing a sequential test to detect the change. Due to the unpredictable and intermittent nature of harvested energy arrivals, the sensors need to decide whether they want to sense, and at what rate they want to quantize their information before sending them to the FC, since higher quantization rates result in higher accuracy and better detection performance, at the cost of higher energy consumption. We formulate an optimal sensing and quantization rate allocation problem (in order to minimize the expected detection delay subject to false alarm rate constraint) based on the availability (at the FC) of non-causal and causal information of sensors’ energy state information, and channel state information between the sensors and the FC. Motivated by the asymptotically inverse relationship between the expected detection delay (under a vanishingly small probability of false alarm) and the Kullback-Leibler (KL) divergence measure at the FC, we maximize an expected sum of the KL divergence measure over a finite horizon to obtain the optimal sensing and quantization rate allocation policy, subject to energy causality constraints at each sensor. The optimal solution is obtained using a typical dynamic programming based technique, and based on the optimal quantization rate, the optimal quantization thresholds are found by maximizing the KL information measure per slot. We also provide suboptimal threshold design policies using uniform quantization and an asymptotically optimal quantization policy for higher number of quantization bits. We provide an asymptotic approximation for the loss due to quantization of the KL measure, and also consider an alternative optimization problem with minimizing the expected sum of the inverse the KL divergence measure as the cost per time slot. Numerical results are provided comparing the various optimal and suboptimal quantization strategies for both optimization problem formulations, illustrating the comparative performance of these strategies at different regimes of quantization rates