Talha Ahmed Khan

Millimeter Wave Energy Harvesting

The millimeter wave (mmWave) band, a prime candidate for 5G cellular networks, seems attractive for wireless energy harvesting since it will feature large antenna arrays and extremely dense base station (BS) deployments. The viability of mmWave for energy harvesting though is unclear, due to the differences in propagation characteristics, such as extreme sensitivity to building blockages. This paper considers a scenario where low-power devices extract energy and/or information from the mmWave signals. Using stochastic geometry, analytical expressions are derived for the energy coverage probability, the average harvested power, and the overall (energy-and-information) coverage probability at a typical wireless-powered device in terms of the BS density, the antenna geometry parameters, and the channel parameters. Numerical results reveal several network and device level design insights. At the BSs, optimizing the antenna geometry parameters, such as beamwidth, can maximize the network-wide energy coverage for a given user population. At the device level, the performance can be substantially improved by optimally splitting the received signal for energy and information extraction, and by deploying multi-antenna arrays. For the latter, an efficient low-power multi-antenna mmWave receiver architecture is proposed for simultaneous energy and information transfer. Overall, simulation results suggest that mmWave energy harvesting generally outperforms lower frequency solutions.

Performance Analysis of Cooperative Wireless Networks With Unreliable Backhaul Links

A cooperative wireless network, where a cluster of K single-antenna transmitters jointly serve a single-antenna receiver, is considered. Each transmitter is connected to the control unit (CU) via independent but unreliable backhaul links. The CU sends a common message to each transmitter over backhaul links, which upon successful reception, jointly transmit this message to the intended receiver. To facilitate analysis, a general expression is derived for the complementary cumulative distribution function of a sum of K independent random variables, where each random variable is a product of an exponential and a Bernoulli random variable. This result is applied to find a simple closed-form expression that characterizes the system outage performance as a function of network parameters and node geometry. The analytical model is validated using numerical simulations. As an application, the derived expression is also used for investigating the impact of backhaul assignment on the system performance.

On Wirelessly Powered Communications with Short Packets

Wireless-powered communications will entail short packets due to naturally small payloads, low-latency requirements and/or insufficient energy resources to support longer transmissions. In this paper, a wireless-powered communication system is investigated where an energy harvesting transmitter, charged by a power beacon via wireless energy transfer, attempts to communicate with a receiver over a noisy channel. Leveraging the framework of finite-length information theory, the system performance is analyzed using metrics such as the energy supply probability at the transmitter, and the achievable rate at the receiver. The analysis yields useful insights into the system behavior in terms of key parameters such as the harvest blocklength, the transmit blocklength, the average harvested power and the transmit power. Closed-form expressions are derived for the asymptotically optimal transmit power. Numerical results suggest that power control is essential for improving the achievable rate of the system in the finite blocklength regime.

Energy Coverage in Millimeter Wave Energy Harvesting Networks

Wireless energy harvesting in millimeter wave (mmWave) cellular networks is attractive, thanks to the large antenna arrays and the anticipated dense deployment of these systems. The signal propagation at mmWave frequencies, however, shows peculiar propagation characteristics such as extreme sensitivity to building blockages. This work analyzes the energy harvesting performance at receivers powered by a mmWave cellular network. Leveraging tools from stochastic geometry, analytical expressions are derived to characterize the energy coverage probability at a typical receiver in terms of the cellular network density, the antenna geometry parameters, and the channel parameters. Results show that there typically exists an optimum transmit antenna beamwidth that maximizes the network-wide energy coverage probability for many operating scenarios. Simulation results further suggest that mmWave energy harvesting could provide a substantial performance boost compared to lower frequency solutions.

Wirelessly Powered Communication Networks With Short Packets

Wirelessly powered communications will entail short packets due to naturally small payloads, low-latency requirements, and/or insufficient energy resources to support longer transmissions. In this paper, a wireless-powered communication system is investigated, where an energy harvesting transmitter, charged by power beacons via wireless energy transfer, attempts to communicate with a receiver over a noisy channel. Under a save-then-transmit protocol, the system performance is characterized using metrics, such as the energy supply probability at the transmitter, and the achievable rate at the receiver for the case of short packets. The analytical treatment is provided for two cases: a three-node setup with a single power beacon and a large-scale network with multiple power beacons. Leveraging finite-length information theory, tractable analytical expressions are derived for the considered metrics in terms of the harvest blocklength, the transmit blocklength, the harvested power, the transmit power, and the network density. The analysis provides several useful design guidelines. Though using a small transmit power or a small transmit blocklength helps avoid energy outages, the consequently smaller signal-to-noise ratio or the fewer coding opportunities may cause a data decoding error. Scaling laws are derived to capture this inherent tradeoff between the harvest and transmit blocklengths. Numerical results reveal that power control is essential for improving the achievable rate of the considered system. The asymptotically optimal transmit power yields nearly optimal performance in the finite blocklength regime.

Wireless Power Transfer in Millimeter Wave Tactical Networks

Wireless power transfer may enable remotely powered operation of low-energy devices in challenging environments such as tactical networks. Millimeter wave (mmWave) is a possible candidate for wireless power transfer, thanks to the use of directional antenna arrays. This letter presents a feasibility study of using mmWave for wireless power transfer in a large-scale network consisting of power beacons and energy harvesters. Using stochastic geometry, system performance is characterized while treating the network nodes as potential blockages to mmWave signals. A link-level metric (energy coverage probability) and a network-level metric (overall success probability) are considered. The former captures whether a typical energy transfer link provides the requisite energy, while the latter also takes the network load into account. Numerical simulations suggest that network densification helps improve the performance, despite an increase in the blockage density. For a given density, deploying an optimal fraction of nodes as power beacons maximizes the number of successful energy harvesters. Finally, mmWave outperforms lower frequency solutions despite blockages.

A stochastic geometry analysis of cooperative wireless networks powered by energy harvesting

A large wireless network with energy harvesting transmitters is considered, where a group of K transmitters form a cluster to cooperatively serve a desired user. Using stochastic geometry, simple closed-form expressions are derived to characterize the outage performance as a function of important parameters such as the energy harvesting rate, buffer size and cluster size for a given cluster geometry. The developed framework also allows the K in-cluster transmitters to have different energy harvesting capabilities. A comparison with simulation results reveals that the derived expressions closely model the signal-to-interference-and-noise ratio distribution at the receiver, particularly in the low-outage regime. Lastly, the developed framework is used to investigate the impact of different parameters such as cluster and buffer size on outage performance.

A Stochastic Geometry Approach to Analyzing Cellular Networks with Semi-Static Clustering

Static base-station clustering allows clustered transmitters to jointly serve a group of users and thus eliminate the intra-cluster interference. The network performance is then bottlenecked by the cluster-edge users. Semi-static clustering can help improve the performance along the cluster edges by time-sharing between different clustering patterns. We propose a simple clustering and user scheduling algorithm to gauge the performance gain of semi-static clustering. Under a stochastic geometry framework, we derive analytical expressions for the coverage and rate of a user at a given location. As the cluster size goes to infinity, we show that the outage probability of semi-static clustering decays at the same order as that of static clustering. Thus, in the asymptotic regime, the performance gain provided by semi- static clustering can be characterized by a linear factor. Numerical results demonstrate the gain of semi-static clustering in the non-asymptotic regime.

Energy Efficiency of Wireless Information and Power Transfer with Massive MIMO

Massive MIMO, a building block of future 5G systems, is attractive for wireless information and energy transfer. This is largely due to its ability to focus energy towards desired spatial locations. In this paper, the overall energy efficiency of a wirelessly powered massive MIMO system is investigated where a multi-antenna base-station uses wireless energy transfer to charge single- antenna energy harvesting users on the downlink. The users exploit the harvested energy to transmit information to the base-station on the uplink. Using a scalable model for the circuit power consumption at the base-station, the energy efficiency performance (measured in bits/joule) of the overall system is characterized. A closed-form expression is derived for the energy efficiency- optimal downlink transmit power in terms of the key system parameters such as the number of base- station antennas and the number of users. Simulation results suggest that it is energy efficient to operate the system in the massive MIMO regime. As the number of antennas becomes large, increasing the transmit power as well as serving more users help improve the energy efficiency for moderate to large number of antennas.

A Stochastic Geometry Analysis of Large-Scale Cooperative Wireless Networks Powered by Energy Harvesting

Energy harvesting is an emerging technology for enabling green, sustainable, and autonomous wireless networks. In this paper, a large-scale wireless network with energy harvesting transmitters is considered, where a group of transmitters forms a cluster to cooperatively serve a desired receiver amid interference and noise. To characterize the link-level performance, closed-form expressions are derived for the transmission success probability at a receiver in terms of key parameters such as node densities, energy harvesting parameters, channel parameters, and cluster size, for a given cluster geometry. The analysis is further extended to characterize a network-level performance metric, capturing the tradeoff between link quality and the fraction of receivers served. Numerical simulations validate the accuracy of the analytical model. Several useful insights are provided. For example, while more cooperation helps improve the link-level performance, the network-level performance might degrade with the cluster size. Numerical results show that a small cluster size (typically 3 or smaller) optimizes the network-level performance. Furthermore, substantial performance can be extracted with a relatively small energy buffer. Moreover, the utility of having a large energy buffer increases with the energy harvesting rate as well as with the cluster size in sufficiently dense networks.