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To meet the ever-growing mobile data traffic, network spatial densification
with various low-power nodes in addition to the conventional high-power macro base
stations (BS), a.k.a. heterogeneous network (HetNet), is regarded as one key
enabling solution. Due to the unplanned nature, HetNets are very irregular and
severe interference can happen without judicious designs of the user association
rules. Conventional maximum downlink (DL) signal-to-interference-plus-noise ratio
(SINR) association rule, on the other hand, can not fully release the traffic


Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which use electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena.


We formulate and study a multi-user multi-armed bandit (MAB) problem that exploits the temporal-spatial reuse of primary user (PU) channels so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve a NP-complete optimization problem at exponentially increasing time intervals.


The path-loss exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction.


This work examines the charging power allocation and beam selection problem for distributed estimation in wireless passive sensor networks, where the sensors are charged over the air by RF-enabled energy sources. A two-phase replenishment and transmission cycle is considered. In the replenishment phase, each wireless charger emits power over the air through a carefully selected beam and power to replenish the wireless sensors.


This paper investigates the use of wireless power transfer (WPT) for {measurement sensing} and information transmission in a wireless sensor network (WSN) performing distributed parameter estimation using an adaptive diffusion least mean-squares (LMS) strategy. We consider a hybrid WSN consisting of common sensor nodes (CNs) and super sensor nodes (SNs) that are capable of WPT. In each diffusion iteration, all nodes sense measurements and exchange parameter estimates with their neighbors. Each SN also transfers wireless power to its neighboring CNs via beamforming.