A Stochastic Geometry-based Demand Response Management Framework for Cellular Networks Powered by Smart Grid

In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized $\alpha$-fair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.Comment: 6 pages, 4 figure

Coexistence of RF-powered IoT and a Primary Wireless Network with Secrecy Guard Zones

This paper studies the secrecy performance of a wireless network (primary network) overlaid with an ambient RF energy harvesting IoT network (secondary network). The nodes in the secondary network are assumed to be solely powered by ambient RF energy harvested from the transmissions of the primary network. We assume that the secondary nodes can eavesdrop on the primary transmissions due to which the primary network uses secrecy guard zones. The primary transmitter goes silent if any secondary receiver is detected within its guard zone. Using tools from stochastic geometry, we derive the probability of successful connection of the primary network as well as the probability of secure communication. Two conditions must be jointly satisfied in order to ensure successful connection: (i) the SINR at the primary receiver is above a predefined threshold, and (ii) the primary transmitter is not silent. In order to ensure secure communication, the SINR value at each of the secondary nodes should be less than a predefined threshold. Clearly, when more secondary nodes are deployed, more primary transmitters will remain silent for a given guard zone radius, thus impacting the amount of energy harvested by the secondary network. Our results concretely show the existence of an optimal deployment density for the secondary network that maximizes the density of nodes that are able to harvest sufficient amount of energy. Furthermore, we show the dependence of this optimal deployment density on the guard zone radius of the primary network. In addition, we show that the optimal guard zone radius selected by the primary network is a function of the deployment density of the secondary network. This interesting coupling between the two networks is studied using tools from game theory. Overall, this work is one of the few concrete works that symbiotically merge tools from stochastic geometry and game theory

Joint Uplink and Downlink Coverage Analysis of Cellular-based RF-powered IoT Network

Ambient radio frequency (RF) energy harvesting has emerged as a promising solution for powering small devices and sensors in massive Internet of Things (IoT) ecosystem due to its ubiquity and cost efficiency. In this paper, we study joint uplink and downlink coverage of cellular-based ambient RF energy harvesting IoT where the cellular network is assumed to be the only source of RF energy. We consider a time division-based approach for power and information transmission where each time-slot is partitioned into three sub-slots: (i) charging sub-slot during which the cellular base stations (BSs) act as RF chargers for the IoT devices, which then use the energy harvested in this sub-slot for information transmission and/or reception during the remaining two sub-slots, (ii) downlink sub-slot during which the IoT device receives information from the associated BS, and (iii) uplink sub-slot during which the IoT device transmits information to the associated BS. For this setup, we characterize the joint coverage probability, which is the joint probability of the events that the typical device harvests sufficient energy in the given time slot and is under both uplink and downlink signal-to-interference-plus-noise ratio (SINR) coverage with respect to its associated BS. This metric significantly generalizes the prior art on energy harvesting communications, which usually focused on downlink or uplink coverage separately. The key technical challenge is in handling the correlation between the amount of energy harvested in the charging sub-slot and the information signal quality (SINR) in the downlink and uplink sub-slots. Dominant BS-based approach is developed to derive tight approximation for this joint coverage probability. Several system design insights including comparison with regularly powered IoT network and throughput-optimal slot partitioning are also provided

Power Beacon-Assisted Millimeter Wave Ad Hoc Networks

Deployment of low cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beam-width and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in a mmWave ad hoc network using PBs.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Wirelessly Powered Backscatter Communication Networks: Modeling, Coverage and Capacity

Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering this massive number of embedded devices is a key challenge of designing IoT since batteries increase the devices' form factors and battery recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that enables D2D communication between passive nodes by integrating wireless power transfer and backscatter communication, which is called a wirelessly powered backscatter communication (WP-BackCom) network. In the network, standalone power beacons (PBs) are deployed for wirelessly powering nodes by beaming unmodulated carrier signals to targeted nodes. Provisioned with a backscatter antenna, a node transmits data to an intended receiver by modulating and reflecting a fraction of a carrier signal. Such transmission by backscatter consumes orders-of-magnitude less power than a traditional radio. Thereby, the dense deployment of low-complexity PBs with high transmission power can power a large-scale IoT. In this paper, a WP-BackCom network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad-hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. The backscatter nodes can harvest energy from and backscatter carrier signals transmitted by PBs. Furthermore, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of backscatter parameters, including backscatter duty cycle and reflection coefficient, as well as the PB density. The effects of the parameters on network performance are characterized.Comment: 28 pages, 11 figures, has been submitted to IEEE Trans. on Wireless Communicatio

Wireless Power Transfer in Massive MIMO Aided HetNets with User Association

This paper explores the potential of wireless power transfer (WPT) in massive multiple input multiple output (MIMO) aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as possible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze two user association schemes. We adopt the linear maximal ratio transmission beam-forming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then we derive the average uplink achievable rate under the harvested energy constraint.Comment: 36 pages, 11 figures, to appear in IEEE Transactions on Communication

Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio

Multimedia content especially videos is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge has emerged to be a solution for low-latency content delivery. Compared with the traditional wireless communication, content delivery has a key characteristic that many signals coexisting in the air carry identical popular content. They, however, can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels following the classic approach. To address this issue, we present a novel idea of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures that all signals carry identical content are encoded using an identical MAC scheme, achieving spatial MAC alignment. Consequently, interference can be harnessed as signals, to improve the reliability of wireless delivery. In the remaining part of the paper, we focus on quantifying the gain CAMAC can bring to a content-delivery network using a stochastic-geometry model. Specifically, content helpers are distributed as a Poisson point process, each of which transmits a file from a content database based on a given popularity distribution. It is discovered that the successful content-delivery probability is closely related to the distribution of the ratio of two independent shot noise processes, named a shot-noise ratio. The distribution itself is an open mathematical problem that we tackle in this work. Using stable-distribution theory and tools from stochastic geometry, the distribution function is derived in closed form. Extending the result in the context of content-delivery networks with CAMAC yields the content-delivery probability in different closed forms. In addition, the gain in the probability due to CAMAC is shown to grow with the level of skewness in the content popularity distribution.Comment: 32 pages, to appear in IEEE Trans. on Wireless Communicatio