This work examines the trajectory optimization of an unmanned aerial vehicle (UAV) for the purpose of data-gathering from a backscattering wireless sensor network. The sensors are assumed to be remotely powered by distributed power-beacons using wireless power transfer (WPT) technology. The energy signals are backscattered towards the UAV, carrying information about the sensors’ observations. Under a strict deadline constraint, the number of time slots that can be used to gather data is limited and, thus, the sensors must carefully determine their activation time slots according to the UAV’s position at given times. The UAV trajectory and sensor activation decisions are coupled and, thus, jointly determined by minimizing the mean-squared error of the reconstructed sensor observations at the UAV. The trajectory is constrained by the UAV’s maximum flight speed and minimum altitude, and the sensors’ transmissions suffer from altitude-dependent path loss. An iterative procedure is proposed where the UAV trajectory, elevation angle and sensor activation are updated in turn until convergence. Performance comparison is provided through numerical simulations.

This work examines trajectory learning, clustering, and user association policies for dynamically connectable unmanned aerial vehicle base stations (UAV-BSs). Here, UAV-BSs are allowed to dynamically form physically-connected collocated antenna arrays that enable joint transmission and cooperative energy sharing among the connected UAV-BSs. The UAVs' trajectories, their clustering decisions, and the user association are jointly determined for the case with static users by maximizing a proportional-fair objective given by the sum log-rate of all users. Then, for the case with dynamic users, the UAVs' trajectory learning, re-clustering, and user handover policies are developed to adapt to changes in the users' locations. In particular, the UAVs' locations are adjusted gradually in each time slot based on a stochastic gradient ascent algorithm, and user handover decisions are made in each time slot based on a reward function that is inspired by the solution in the static case. The clustering decisions are updated every T time slots by combining two UAV clusters or by separating a cluster into two. To determine the transmission schemes in each time slot, a semi-orthogonal user scheduling policy is first employed to determine the users that are served in each time slot. Then, joint design of the transmit beamformers and powers is proposed to maximize the sum of log signal-to-leakage-plus-noise ratio (SLNR) of scheduled users. Simulations are provided to demonstrate the effectiveness of the proposed schemes.

This work proposes a deep-learning (DL) based coordinated precoder design for multicell downlink systems with rate-limited exchange of channel state information (CSI) among base-stations (BSs). Two CSI compression techniques are proposed, one based on a binarized convolutional neural network (CNN) and one based on a learned vector-quantization (VQ) codebook. The former utilizes a CNN-based CSI feature extractor to directly compute the binary feature vector that is to be exchanged with other BSs. The latter utilizes a DL-based VQ codebook to encode the CSI feature vector that is obtained at the output of the feature extractor. In both cases, each BS takes the rate-limited CSI received from other BSs as input to a precoder network that produces the normalized precoding vectors and the transmit powers using a multitask learning architecture. By using solutions of the weighted minimum mean square error (WMMSE) algorithm as the output labels, end-to-end training of both the CSI compression and transmit precoder networks is performed jointly at all BSs. By doing so, the CSI compression networks will be able to extract the CSI features that are most effective for precoder computation at the BSs. Our simulation results show that the proposed schemes can achieve weighted sum rates close to that in the full CSI scenario, even when the number of exchanged bits is small, and outperform existing random VQ methods.

This paper proposes a dynamic precoding and power allocation policy for mutually cooperative device-to-device (D2D) transmitter-receiver pairs that underlay a cellular system in the uplink. The cooperative transmission consists of two phases: a data-sharing phase (i.e., phase 1) and a joint transmission phase (i.e., phase 2). Multicast precoders are used in phase 1 and coordinated block-diagonalization precoders are considered in phase 2. The precoders are jointly designed to maximize the long-term utility of the D2D users subject to long-term individual power and rate-gain constraints and an instantaneous interference constraint at the base-station. The long-term objective and constraints allow cooperating users to adapt their resources more flexibly over time, but increase the complexity of the design. By adopting the Lyapunov optimization framework and by constructing virtual queues to record the temporal evolution of the system states, the long-term utility maximization problem can be decoupled into a series of short-term weighted-rate-minus-energy-penalty (WRMEP) optimization problems that can be solved efficiently. A low-complexity algorithm is further proposed for solving the WRMEP problem when multicasting in the data-sharing phase is performed by a spatially white input. Theoretical performance guarantees and a bound on the virtual queue backlogs are also derived.

This work examines joint beamforming and power allocation schemes as well as trajectory learning and clustering mechanisms for dynamically connectable unmanned aerial vehicle (UAV) base-stations (BSs). Here, UAV-BSs are allowed to join together dynamically and form physically-connected collocated antenna arrays that enable joint transmission and cooperative energy sharing among the connected UAVs. A joint design of the transmit powers and beamformers in each time slot is first proposed based on the maximization of the sum signal-to-leakageplus-noise-ratio (SLNR) of all users. The solution is obtained via alternating optimization where the transmit powers and beamformers are optimized in turn until convergence. Then, based on the per-time-slot design, a dynamic UAV moving and clustering policy is proposed where the expected sum rate of the system is maximized while adapting to changes in the users' locations and environment. Here, UAV locations are adjusted gradually in each time slot according to the stochastic gradient of the expected system sum rate. The UAV clusters are updated every T time slots by combining the two clusters that yield the maximum increase in the expected system sum rate. Simulations are provided to demonstrate the effectiveness of the proposed schemes.

In this work, coordinated beamforming and power allocation schemes are proposed for multicell downlink non-orthogonal multiple access (NOMA) systems. Beamforming is adopted at the base-station to mitigate both intercell and intracell interference whereas power-domain multiplexing between a pair of users is considered in each spatial dimension. Given the beamforming vectors, intra-pair power allocation is first determined by maximizing the weighted sum rate under worst-case interference. Then, two coordinated beamforming schemes that take into consideration the effect of power-domain multiplexing are proposed, namely, the coordinated scheduling with pair-wise zero-forcing (CSPZF) and the maximum pair-wise signal-to-leakage-plus-noise ratio (PSLNR) beamforming schemes. CSPZF beamforming utilizes the available spatial degrees of freedom at each base-station to separate signals intended for intracell user pairs as well as to eliminate strong out-of-cell interference. PSLNR beamforming, on the other hand, enhances the signal intended for each user pair while suppressing its leakage towards other users (including those in other cells). CSPZF is shown to outperform PSLNR beamforming in the high SNR regime where the performance is interference-limited and, vice versa.

This work proposes a multiuser hybrid beamforming scheme for non-orthogonal multiple access (NOMA) systems using the minimum mean square error (MMSE) approach to weighted sum rate maximization. While NOMA may be effective in terms of enhancing user fairness, hybrid beamforming is necessary to reduce the transceiver cost as the system moves towards higher frequency. The design is divided into two stages. In the first stage, a fully digital multiuser beamformer is derived by maximizing the weighted sum rate of all users under no constraint on the number of RF chains. This problem is then transformed into a weighted sum MSE minimization problem, which facilitates the use of alternating optimization to obtain an efficient local solution. In the second stage, the previously obtained multiuser beamformer is then split into RF and baseband beamformers by using orthogonal matching pursuit (OMP). A user role selection algorithm is then proposed to determine the role of strong and weak users. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

This work examines the user-pair selection problem for distributed-input distributed-output (DIDO) wireless systems. A DIDO system refers to a network of densely deployed transmitter and receiver pairs, where the transmitters are connected to and coordinated by a DIDO server. The system sum rate is known to grow without bound as the number of transmitter-receiver pairs increases. However, when zero-forcing (ZF) beamforming is adopted across the transmitters (as assumed in most existing works on DIDO), the effect of power amplification due to ill-conditioned channel matrices may significantly reduce the system sum rate. In this work, a decremental user-pair selection (DUPS) algorithm is proposed to determine the set of transmitter-receiver pairs that should be simultaneously active in order to reduce the impact of power amplification and increase the system sum rate. A low-complexity variant of DUPS is also proposed and an asymptotic lower bound of its sum rate is derived using extreme value theory. Moreover, inspired by the semi-orthogonal user selection (SUS) algorithm, often adopted in the conventional multiple-input multiple-output (MIMO) literature, a semi-orthogonal DUPS algorithm is also proposed by taking into consideration the orthogonality of the users' channel vectors in the selection process. Simulations are provided to demonstrate the effectiveness of the proposed schemes.

Zero-forcing (ZF) beamforming is a practical linear transmission scheme that eliminates inter-user interference in the downlink of a multiuser multiple-input single-output (MISO) wireless system. By considering base-stations (BSs) that are supported by renewable energy, this work examines offline and online ZF beamforming designs based on two different objectives, namely, sum-rate maximization and energy-cost minimization. For offline policies, the channel states and the energy arrivals are assumed to be known a priori for all time instants whereas, in the online policies, only causal information is available. The designs are subject to energy causality and energy storage constraints, i.e., the constraint that energy cannot be used before it arrives and the constraint that the stored energy cannot exceed the maximum battery storage capacity. In the sum-rate maximization problem, the base-station is assumed to be supported only by renewable energy and the goal is to maximize the sum rate over all users by a predetermined deadline. The optimization of the ZF beamforming direction and power allocation can be decoupled, and the solutions can be found exactly. In the energy-cost minimization problem, the base-station is assumed to be supported by both renewable and power-grid energy, and the goal is to minimize the cost of purchasing grid energy subject to quality-of-service constraints at the users. The problem can be formulated as a convex optimization problem and can be solved efficiently using off-the-shelf solvers. Offline solutions are first obtained and the intuitions gained from their results are used to derive effective online policies. The effectiveness of the proposed policies are demonstrated through computer simulations.

This work examines offline and online power control policies for efficient usage of renewable energy in the downlink of a multi-antenna wireless system. Two multiuser beamforming schemes are considered, namely, channel inversion (CI) and maximal ratio transmit (MRT) beamforming schemes. Power control policies are derived for both schemes, respectively, with the goal of maximizing the sum throughput by a deadline subject to energy causality and battery storage constraints. With CI beamforming, the power control problem can be formulated as a convex optimization problem, whose solution can be obtained using the directional water-filling algorithm. With MRT beam-forming, the power control problem becomes non-convex, due to interference between the signals intended for different users, and, thus, is difficult to solve exactly. However, an efficient solution can be obtained by performing successive approximation into a sequence of geometric programming problems, i.e., by employing the condensation method. Offline power control policies are first derived assuming non-causal knowledge of the energy arrival and channel coefficients over time. Online power control policies are then proposed based on observations gained from the offline policy. The performance of the proposed schemes are demonstrated through numerical simulations.

We examine a cooperative uplink CDMA network where multiple sources simultaneously access the cooperative channel using different spreading codes and compete for the resources at the relays. To efficiently utilize the limited energy and bandwidth resources at the relays, we propose in this work a decentralized reduced-rank multiuser relaying (RR-MUR) where the data received at each relay is compressed into a limited number of dimensions and forwarded with optimal power allocation among the different dimensions. The proposed scheme follows upon our previous work where a centralized strategy has been proposed. Specifically, we propose the minimum mean square error principle component analysis (MMSE-PCA) approach that can be used to design the relay precoders in a decentralized manner. We show that the MMSE-PCA scheme is the optimal relay precoder design when only one relay exists in the network. Through numerical simulations, we show that the proposed scheme outperforms the Q-selection scheme, where only a selected group of sources are served by each relay.

Physical-layer secrecy in wireless fading channels has been studied extensively in recent years to ensure reliable communication between the transmitter and the receiver subject to constraints on the information attainable by the eavesdropper. With multiple antennas at the transmitter, Goel and Negi proposed the use of artificial noise (AN) in the null space of the receiver's channel to corrupt the eavesdropper's reception, which helps guarantee secrecy without knowledge of the eavesdropper's channel. It has been shown that the secrecy capacity can be made arbitrarily large by increasing the transmission power, when perfect knowledge of the receiver's channel direction information (CDI) is available. However, in practice, this is not possible due to rate-limitations on the feedback channel. This paper studies the impact of quantized channel feedback on the secrecy capacity achievable with artificial noise. We show that, with imperfect CDI at the transmitter, the AN that was originally intended only for the eavesdropper may leak into the receiver's channel and limit the achievable secrecy rate. To maintain a constant performance degradation, the number of feedback bits must increase at least logarithmically with the transmission power. Moreover, we observe that the portion of power allocated to the transmission of AN should decrease as the number of quantization bits decreases to alleviate the degradation due to noise leakage.

Cooperative relaying has been studied extensively in the literature to exploit spatial diversity gains by having each source transmit its messages through multiple independently fading relay paths. In multiuser systems where multiple sources may access the same set of relays simultaneously, CDMA spreading techniques along with multiuser detection schemes have been proposed in the literature to eliminate multiple access interference (MAI). In order for each relay to forward messages from all sources, a tremendous increase in dimensions (or spreading gain) is used to accommodate the relay transmissions. To reduce the required bandwidth or dimensions, we propose a reduced-rank multiuser relaying (RR-MUR) scheme where the data received from multiple users are first compressed into lower dimensions before being retransmitted. More specifically, linear compression precoders at the relays and decoder at the destination are found by imposing a recursive joint optimization procedure with the objective of minimizing the mean square error (MMSE) of the estimate at the destination. We show through numerical simulations that the RR-MUR scheme outperforms the often adopted Q-selection scheme in terms of increased spectral efficiency.

Multi-photon fluorescence microscopy has been cited for its advantage in increased depth penetration due to low linear absorption and scattering coefficient of biological specimen in the near infrared (NIR) range. Because of the need of high peak power for efficiently exciting two-photon fluorescence, the relationship between cell damage and peak power has become an interesting and much debated topic in the applications of multi-photon fluorescence microscopy. It is conceivable that at high illumination intensity, non-linear photochemical processes have impacts on cell physiology and viability in ways much different from low illumination in the linear domain. In this article, we discuss some of the issues in two-photon fluorescence microscopy, including the degree of transparency of the specimen, a comparison of single- and two-photon excited fluorescence spectra, and the cell damage under high intensity illumination, using plant cells as a model.