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We consider a network, tasked with solving binary distributed detection, consisting of N sensors, a fusion center (FC), and a feedback channel from the FC to sensors. Each sensor is capable of harvesting energy and is equipped with a finite size battery to store randomly arrived energy. Sensors process their observations and transmit their symbols to the FC over orthogonal fading channels. The FC fuses the received symbols and makes a global binary decision. We aim at developing adaptive channel-dependent transmit power control policies such that J-divergence based detection metric is maximized at the FC, subject to total transmit power constraint. Modeling the quantized fading channel, the energy arrival, and the battery dynamics as time-homogeneous finite-state Markov chains, and the network lifetime as a geometric random variable, we formulate our power control optimization problem as a discounted infinite-horizon constrained Markov decision process (MDP) problem, where sensors' transmit powers are functions of the battery states, quantized channel gains, and the arrived energies. We utilize stochastic dynamic programming and Lagrangian approach to find the optimal and sub-optimal power control policies. We demonstrate that our sub-optimal policy provides a close-to-optimal performance with a reduced computational complexity and without imposing signaling overhead on sensors.

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), tasked with solving a binary distributed detection problem. Sensors communicate directly with the FC over orthogonal fading channels. Each sensor can harvest randomly arriving energy and store it in a battery. Also, it knows its quantized channel state information (CSI), acquired via a limited feedback channel from the FC. We propose a transmit power control strategy such that the J-divergence based detection metric is maximized, subject to an average transmit power per sensor constraint. The proposed strategy is parametrized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals, to strike a balance between the rates of energy harvesting and energy consumption for data transmission. This strategy allows each sensor to adapt its transmit power based on its battery state and its qunatized CSI. Finding the optimal strategy requires solving a non-convex optimization problem that is not differentiable with respect to the optimization variables.We propose near-optimal strategy based on hybrid search methods that have a low-computational complexity., Comment: arXiv admin note: substantial text overlap with arXiv:2103.02742

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), tasked with detecting a known signal in uncorrelated Gaussian noises. Each sensor can harvest randomly arriving energy and store it in a finite-size battery. Sensors communicate directly with the FC over orthogonal fading channels. Each sensor adapts its transmit symbol power, such that the larger its stored energy and its quantized channel gain are, the higher its transmit symbol power is. To strike a balance between energy harvesting and energy consumption, we formulate two constrained optimization problems (P1) and (P2), where in both problems we seek the jointly optimal local decision thresholds and channel gain quantization thresholds. While in (P1) we maximize the Jdivergence of the received signal densities at the FC, in (P2) we minimize the average total transmit power, subject to certain constraints. We solve (P1) and (P2), assuming that the batteries reach their steady-state. Our simulation results demonstrate the effectiveness of our optimization on enhancing the detection performance in (P1), and lowering the average total transmit power in (P2). They also reveal how the energy harvesting rate, the battery size, the sensor observation and communication channel parameters impact obtained solutions.

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth-limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received signal densities under two hypotheses at the FC is maximized, subject to certain (battery and power) constraints. We derive the optimal power map, which depends on the energy arrival rate, the battery capacity, and the battery states probabilities at the steady state. Using the optimal power map, each sensor optimally adapts its transmit power, based on its battery state and its channel state. Our simulation results demonstrate the performance of our proposed power adaptation scheme for different system parameters.

We consider a wireless sensor network, consisting of K heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting and storing energy for communication with the FC. For energy efficiency, a sensor transmits only if the sensor test statistic exceeds a local threshold {\theta}k, its channel gain exceeds a minimum threshold, and its battery state can afford the transmission. Our proposed transmission model at each sensor is motivated by the channel inversion power control strategy in the wireless communication community. Considering a constraint on the average energy of transmit symbols, we study the optimal {\theta}_k's that optimize two detection performance metrics: (i) the detection probability P_D at the FC, assuming that the FC utilizes the optimal fusion rule based on Neyman-Pearson optimality criterion, and (ii) Kullback-Leibler distance (KL) between the two distributions of the received signals at the FC conditioned by each hypothesis. Our numerical results indicate that {\theta}k's obtained from maximizing the KL distance are near-optimal. Finding these thresholds is computationally efficient, as it requires only K one-dimensional searches, as opposed to a K-dimensional search required to find the thresholds that maximize P_D.

Molecular Communications via Diffusion (MCvD) is a promising paradigm which enables nano-machines to communicate with each other. However, the reliability of existing systems degrade rapidly as the distance between the transmitters and the receivers grows. To solve this issue, relaying schemes must be implemented in practice. In this paper, we study two relaying schemes: In the first case, the relay node decodes the incoming signal symbol and forwards it to the receiver using a different type of molecule. Then, the receiver detects the information bits by only considering the molecules from the relay node. In the second case, the receiver considers both the types of molecules sent from the transmitter and the relay node. For these two scenarios, the optimal location of the relay node is obtained. We assume Quadruple Concentration Shift Keying (QCSK) modulation in which the signal is encoded into the four level concentrations of molecules emitted by the nano-machines. Our simulation results indicate that adding a relay improves the performance by 10dB and 15dB in the first and the second schemes, respectively., Comment: arXiv admin note: text overlap with arXiv:1401.3410, arXiv:1410.5585, arXiv:1504.03738, arXiv:1410.4258 by other authors