## Featured Publications and Upcoming Events

### Upcoming Conferences

#### Globecom, December 4 - 8, 2016

The following Wireless @ Virginia Tech faculty and students will be presenting at Globecom:

Title: Quantum Game Theory for Beam Alignment in Millimeter Wave Device-to-Device Communications

Abstract:

In this paper, the problem of optimized beam alignment for wearable device-to-device (D2D) communications over millimeter wave (mmW) frequencies is studied. In particular, a noncooperative game is formulated between wearable communication pairs that engage in D2D communications. In this game, wearable devices acting as transmitters autonomously select the directions of their beams so as to maximize the data rate to their receivers. To solve the game, an algorithm based on best response dynamics is proposed that allows the transmitters to reach a Nash equilibrium in a distributed manner. To further improve the performance of mmW D2D communications, a novel quantum game model is formulated to enable the wearable devices to exploit new quantum directions during their beam alignment so as to further enhance their data rate. Simulation results show that the proposed game-theoretic approach improves the performance, in terms of data rate, of about 75% compared to a uniform beam alignment. The results also show that the quantum game model can further yield up to 20% improvement in data rates, relative to the classical game approach.

Title: Online Channel Allocation for Full-Duplex Device-to-Device Communications

Authors: Gilsoo Lee, PhD student, Wa

Abstract:

Full-duplex device-to-device (D2D) communications over cellular networks is a promising solution for maximizing wireless spectral efficiency. However, in practice, due to the unpredictable arrival of D2D users, the base station (BS) must smartly allocate suitable channels to arriving D2D pairs. In this paper, the problem of dynamic channel allocation is studied for full-duplex D2D networks. In particular, the goal of the proposed approach is to maximize the system sum-rate under complete uncertainty on the arrival process of D2D users. To solve this problem, a novel approach based on an online weighted bipartite matching is proposed. To find the desired solution of the channel allocation problem, a greedy online algorithm is developed to enable the BS to decide on the channel assignment for each D2D pair, without knowing any prior information on future D2D arrivals. For an illustrative case study, upper and lower bounds on the competitive ratio that compares the performance of the proposed online algorithm to that of an offline algorithm are derived. Simulation results show that the proposed online algorithm can achieve a near-optimal sum-rate with an optimality gap that is no higher than 8.3% compared to the offline, optimal solution that has complete knowledge of the system.

Title: “Mobile Internet of Things: Can UAVs provide an energy-efficient mobile architecture

Abstract:

In this paper, the optimal trajectory and deployment of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum energy consumption, a new approach for optimal mobility of the UAVs is proposed. First, given a fixed ground IoT network, the total transmit power of the devices is minimized by properly clustering the IoT devices with each cluster being served by one UAV. Next, to maintain energy-efficient communications in time-varying mobile IoT networks, the optimal trajectories of the UAVs are determined by exploiting the framework of optimal transport theory. Simulation results show that by using the proposed approach, the total transmit power of IoT devices for reliable uplink communications can be reduced by 56% compared to the fixed Voronoi deployment method. Moreover, our results yield the optimal paths that will be used by UAVs to serve the mobile IoT devices with a minimum energy consumption. -

Other recent publications by this author

Title: "Resource Allocation and Coordination for Critical Messages using Finite Memory Learning

Abstract:

In this paper, a novel framework for enabling machine-to-machine (M2M) communications between machine type devices (MTDs) having heterogeneous message types and under stringent resource constraints is proposed. In particular, an M2M communication system is considered in which two types of messages must seamlessly coexist: periodic, delay tolerant messages, such as meter readings, and critical, real-time messages that indicate the occurrence of critical events, such as a fire or a demand response. Due to the unpredictable occurrence of the critical messages, the MTDs must autonomously learn how to adjust their transmission parameters to ensure the timely delivery of the critical messages to the base station (BS). To address this problem, a novel approach based on the framework of sequential learning with finite memory is proposed. In this approach, the MTDs can learn how many critical messages exist, and collectively allocate the uplink transmission resources needed for the critical messages. Moreover, the proposed learning approach does not require the MTDs to be omniscient and only requires partial, finite information. The bounds on effectiveness of the proposed learning algorithm are derived. Simulation results show that, using the proposed scheme, the probability of successful transmission can quickly reach 99\% with minimal memory requirement. The results also show that the proposed learning framework can quickly coordinate the MTDs with critical messages and prevent repeated transmission failures.

Other recent publications by this author

Title: Echo State Networks for Proactive Caching and Content Prediction in Cloud Radio Access Networks.

Abstract:

Proactive caching at the baseband units (BBUs) in cloud radio access networks (CRANs) has attracted significant attention. However, most existing works assume certain content distribution while ignoring the massive nature of data in CRANs. In contrast, in this paper, the problem of proactive caching is studied for CRANs. In this model, the BBUs can predict the content distribution of each user, determine which content to cache, and cluster remote radio heads (RRHs) based on the content predictions. This problem is formulated as an optimization problem which jointly incorporates backhaul loads, RRH clustering, and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks with sublinear algorithms is proposed. Using echo state networks, the BBUs can predict users content distribution while having only limited information on the network’s and users’ states. Then, a sublinear algorithm is proposed to determine which content to cache and how to cluster the RRHs while using limited content request samples. Simulation results using real data from Youku show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 26.8% and 36.5%, respectively, compared to random caching and random caching without clustering.

### Other Conference Publications

Title: “Prospect Theory for Enhanced Smart Grid Resilience Using Distributed Energy Storage”

Conference: 54th Annual Allerton Conference on Communication, Control, and Computing; Publication date: September 2016

Authors: G. El Rahi, A. Sanjab, W. Saad, N. B. Mandayam, and H. V. Poor

Abstract:

The proliferation of distributed generation and storage units is leading to the development of local, small-scale distribution grids, known as microgrids (MGs). In this paper, the problem of optimizing the energy trading decisions of MG operators (MGOs) is studied using game theory. In the formulated game, each MGO chooses the amount of energy that must be sold immediately or stored for future emergencies, given the prospective market prices which are influenced by other MGOs' decisions. The problem is modeled using a Bayesian game to account for the incomplete information that MGOs have about each others' levels of surplus. The proposed game explicitly accounts for each MGO's subjective decision when faced with the uncertainty of its opponents' energy surplus. In particular, the so-called framing effect, from the framework of prospect theory (PT), is used to account for each MGO's valuation of its gains and losses with respect to an individual utility reference point. The reference point is typically different for each individual and originates from its past experiences and future aspirations. A closed-form expression for the Bayesian Nash equilibrium is derived for the standard game formulation. Under PT, a best response algorithm is proposed to find the equilibrium. Simulation results show that, depending on their individual reference points, MGOs can tend to store more or less energy under PT compared to classical game theory. In addition, the impact of the reference point is found to be more prominent as the emergency price set by the power company increases.

Title: “On Bounded Rationality in Cyber-Physical Systems Security: Game-Theoretic Analysis with Application to Smart Grid Protection”

Conference: 2016 Cyber-Physical Systems Week Publication; Proc. of IEEE/ACM CPS Week, Workshop on Cyber-Physical Security and Resilience in Smart Grids, Vienna, Austria; Date: April 2016

Authors: A. Sanjab and W. Saad

Abstract:

In this paper, a general model for cyber-physical systems (CPSs), that captures the diffusion of attacks from the cyber layer to the physical system, is studied. In particular, a game-theoretic approach is proposed to analyze the interactions between one defender and one attacker over a CPS. In this game, the attacker launches cyber attacks on a number of cyber components of the CPS to maximize the potential harm to the physical system while the system operator chooses to defend a number of cyber nodes to thwart the attacks and minimize potential damage to the physical side. The proposed game explicitly accounts for the fact that both attacker and defender can have different computational capabilities and disparate levels of knowledge of the system. To capture such bounded rationality of attacker and defender, a novel approach inspired from the behavioral framework of cognitive hierarchy theory is developed. In this framework, the defender is assumed to be faced with an attacker that can have different possible thinking levels reflecting its knowledge of the system and computational capabilities. To solve the game, the optimal strategies of each attacker type are characterized and the optimal response of the defender facing these different types is computed. This general approach is applied to smart grid security considering wide area protection with energy markets implications. Numerical results show that a deviation from the Nash equilibrium strategy is beneficial when the bounded rationality of the attacker is considered. Moreover, the results show that the defender's incentive to deviate from the Nash equilibrium decreases when faced with an attacker that has high computational ability.

### Recent and Upcoming Journals

M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage,” IEEE Communications Letters, 2016

M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Unmanned aerial vehicle with underlaid device-to-device communications: performance and tradeoffs,” IEEE Transactions on Wireless Communications, 2016

T. Park and W. Saad, "Learning with Finite Memory for Machine Type Communication," in 50th Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, March 2016.

Abstract: Machine-type devices (MTDs) will lie at the heart of the Internet of Things (IoT) system. A key challenge in such a system is sharing network resources between small MTDs, which have limited memory and computational capabilities. In this paper, a novel learning \emph{with finite memory} framework is proposed to enable MTDs to effectively learn about each others message state, so as to properly adapt their transmission parameters. In particular, an IoT system in which MTDs can transmit both delay tolerant, periodic messages and critical alarm messages is studied. For this model, the characterization of the exponentially growing delay for critical alarm messages and the convergence of the proposed learning framework in an IoT are analyzed. Simulation results show that the delay of critical alarm messages is significantly reduced up to $94\%$ with very minimal memory requirements. The results also show that the proposed learning with finite memory framework is very effective in mitigating the limiting factors of learning that prevent proper learning procedures.

T. Park, N. Abuzainab, and W. Saad, "Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity", IEEE Access, Special Issue on Optimization for Emerging Wireless Networks: IoT, 5G and Smart Grid Communication Networks, to appear, 2016.

Abstract: For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing solutions to overcome key IoT challenges that include data processing, resource management, coexistence with existing wireless networks, and improved IoT-wide event detection. One of the most promising solutions to address these challenges is via the use of innovative learning frameworks that will enable the IoT devices to operate autonomously in a dynamic environment. However, developing learning mechanisms for the IoT requires coping with unique IoT properties in terms of resource constraints, heterogeneity, and strict quality-of-service requirements. In this paper, a number of emerging learning frameworks suitable for IoT applications are presented. In particular, the advantages, limitations, IoT applications, and key results pertaining to machine learning, sequential learning, and reinforcement learning are studied. For each type of learning, the computational complexity, required information, and learning performance are discussed. Then, to handle the heterogeneity of the IoT, a new framework based on the powerful tools of cognitive hierarchy theory is introduced. This framework is shown to efficiently capture the different IoT device types and varying levels of available resources among the IoT devices. In particular, the different resource capabilities of IoT devices are mapped to different levels of rationality in cognitive hierarchy theory, thus enabling the IoT devices to use different learning frameworks depending on their available resources. Finally, key results on the use of cognitive hierarchy theory in the IoT are presented.

Title: A. Sanjab and W. Saad, "Data Injection Attacks on Smart Grids With Multiple Adversaries: A Game-Theoretic Perspective," in IEEE Transactions on Smart Grid, vol. 7, no. 4, pp. 2038-2049, July 2016.

Abstract: Data injection attacks have emerged as a significant threat on the smart power grid. By launching data injection attacks, an adversary can manipulate the real-time locational marginal prices to obtain economic benefits. Despite the surge of existing literature on data injection, most such works assume the presence of a single attacker and assume no cost for attack or defense. In contrast, in this paper, a model for data injection attacks with multiple adversaries and a single smart grid defender is introduced. To study the interactions between the defender and the attackers, two game models are considered. In the first, a Stackelberg game is proposed in which the defender acts as a leader that can anticipate the actions of the adversaries, that act as followers, before deciding on which measurements to protect. The existence and properties of the Stackelberg equilibrium of this game are studied. To find the equilibrium, a distributed learning algorithm that operates under limited system information is proposed and shown to converge to the game solution. In the second proposed game model, it is considered that the defender cannot anticipate the actions of the adversaries. To this end, a hybrid satisfaction equilibrium-Nash equilibrium game is proposed. To find the equilibrium of this hybrid game, a search-based algorithm is introduced. Numerical results using the IEEE 30-bus system are used to illustrate and analyze the strategic interactions between the attackers and defender. The results show that by defending a very small set of measurements, the grid operator can achieve an equilibrium through which the optimal attacks have no effect on the system. Moreover, the results also show how, at equilibrium, multiple attackers can play a destructive role toward each other by choosing to carry out attacks that cancel each other out, leaving the system unaffected. In addition, the obtained equilibrium strategies under the Stackelberg and the hybrid models are compared while characterizing the amount of loss that the defender endures due to its inability to anticipate the attackers' actions.

### Books

In alphabetical order by author's last name:

#### Dr. Steve Ellingson

Dr. Steve Ellingson has a new book on Radio Systems Engineering, released through Cambridge University Press.

Abstract: "Using a systems framework, this textbook provides a clear and comprehensive introduction to the performance, analysis and design of radio systems for students and practicing engineers. Presented within a consistent framework, the first part of the book describes the fundamentals of the subject: propagation, noise, antennas and modulation. The analysis and design of radios, including RF circuit design and signal processing, is covered in the second half of the book."

Bio: Dr. Ellingson is an associate professor in the Bradley Department of Electrical and Computer Engineering. He received his PhD in Electrical Engineering from Ohio State University and is a core faculty member of Wireless @ Virginia Tech leading the research thrusts in RF Analysis and Technologies. Dr. Ellingson is also an avid amateur radio operator.