@article{liu_zhao_hosseinalipour_gao_huang_dai_2024, title={TDRA: A Truthful Dynamic Reverse Auction for DAG Task Scheduling Over Vehicular Clouds}, volume={73}, ISSN={["1939-9359"]}, DOI={10.1109/TVT.2023.3329141}, abstractNote={Vehicular Clouds (VCs) have attracted tremendous attention for offering commendable computing services to vehicles with computation-intensive tasks. Such tasks are often represented as Directed Acyclic Graphs (DAGs) consisting of several interdependent subtasks and directed edges. Processing of DAG tasks often needs pooling the computation resources of vehicles. However, the selfishness of vehicles prevents them from sharing their resources. To this end, we propose a Truthful Dynamic Reverse Auction (TDRA) mechanism to motivate vehicles to participate in service provisioning. To realize TDRA, we first propose an enumeration-based allocation strategy to optimally allocate subtasks among vehicles and obtain a Vickrey-Clarke-Groves (VCG)-based pricing strategy that can ensure the economic properties of individual rationality and truthfulness. Then, to deal with the high computational complexity of obtaining the optimal solution, we develop a near-optimal Dynamic Bilateral Ranking (DBR) allocation strategy to allocate subtasks within polynomial time and design a critical value-based pricing strategy that can also guarantee the two above-mentioned economic properties. Through simulating real-world movement traces of vehicles, we demonstrate that DBR outperforms the existing benchmarks, and verify our theoretical analysis on the economic properties of our developed pricing strategy.}, number={3}, journal={IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY}, author={Liu, Zhang and Zhao, Yifeng and Hosseinalipour, Seyyedali and Gao, Zhibin and Huang, Lianfen and Dai, Huaiyu}, year={2024}, month={Mar}, pages={4337–4351} }
@article{liu_liwang_hosseinalipour_dai_gao_huang_2023, title={RFID: Towards Low Latency and Reliable DAG Task Scheduling Over Dynamic Vehicular Clouds}, volume={72}, ISSN={["1939-9359"]}, DOI={10.1109/TVT.2023.3266582}, abstractNote={Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic nature of VCs (i.e., limited contact duration among vehicles), caused by vehicles' mobility, poses unique challenges to the execution of computation-intensive applications/tasks with a directed acyclic graph (DAG) structure, where each task consists of multiple interdependent components (subtasks). In this paper, we study the scheduling of DAG tasks over dynamic VCs, where multiple subtasks of a DAG task are dispersed across vehicles and processed by vehicles cooperatively. We formulate DAG task scheduling as a 0-1 integer programming problem, aiming to minimize the overall task completion time while ensuring a high execution success rate, which turns out to be NP-hard. To tackle the problem, we develop a ranking and foresight-integrated dynamic scheduling scheme (RFID). RFID consists of i) a dynamic downward ranking mechanism that sorts the scheduling priority of different subtasks, while explicitly taking into account the sequential execution nature of DAG; ii) a resource scarcity-based priority changing mechanism that overcomes possible performance degradations caused by the volatility of VC resources; and iii) a degree-based weighted earliest finish time mechanism that assigns the subtask with the highest scheduling priority to the vehicle which offers rapid task execution along with reliable transmission links. Simulation results reveal the effectiveness of our proposed scheme in comparison to benchmark methods.}, number={9}, journal={IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY}, author={Liu, Zhang and Liwang, Minghui and Hosseinalipour, Seyyedali and Dai, Huaiyu and Gao, Zhibin and Huang, Lianfen}, year={2023}, month={Sep}, pages={12139–12153} }
@article{rahmati_hosseinalipour_yapici_he_guvenc_dai_bhuyan_2022, title={Dynamic Interference Management for UAV-Assisted Wireless Networks}, volume={21}, ISSN={["1558-2248"]}, url={https://doi.org/10.1109/TWC.2021.3114234}, DOI={10.1109/TWC.2021.3114234}, abstractNote={We investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.}, number={4}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Rahmati, Ali and Hosseinalipour, Seyyedali and Yapici, Yavuz and He, Xiaofan and Guvenc, Ismail and Dai, Huaiyu and Bhuyan, Arupjyoti}, year={2022}, month={Apr}, pages={2637–2653} }
@article{hosseinalipour_azam_brinton_michelusi_aggarwal_love_dai_2022, title={Multi-Stage Hybrid Federated Learning Over Large-Scale D2D-Enabled Fog Networks}, ISSN={["1558-2566"]}, DOI={10.1109/TNET.2022.3143495}, abstractNote={Federated learning has generated significant interest, with nearly all works focused on a “star” topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through the network dimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra-and inter-layer model learning that considers the network as a multi-layer cluster-based structure. MH-FL considers the topology structures among the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes a semi-decentralized architecture for federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to form local consensus on the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm for MH-FL to tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages of MH-FL in terms of resource utilization metrics.}, journal={IEEE-ACM TRANSACTIONS ON NETWORKING}, author={Hosseinalipour, Seyyedali and Azam, Sheikh Shams and Brinton, Christopher G. and Michelusi, Nicolo and Aggarwal, Vaneet and Love, David J. and Dai, Huaiyu}, year={2022}, month={Feb} }
@article{hosseinalipour_dai_2021, title={A Two-Stage Auction Mechanism for Cloud Resource Allocation}, volume={9}, ISSN={["2168-7161"]}, DOI={10.1109/TCC.2019.2901785}, abstractNote={The contemporary literature on cloud resource allocation is mostly focused on studying the interactions between customers and cloud managers. Nevertheless, the recent growth in the customers’ demands and the emergence of private cloud providers (CPs) entice the cloud managers to rent extra resources from the CPs so as to handle their backlogged tasks and attract more customers. This also renders the interactions between the cloud managers and the CPs an important problem to study. In this paper, we investigate both interactions through a two-stage auction mechanism. For the interactions between customers and cloud managers, we adopt the options-based sequential auctions (OBSAs) to design the cloud resource allocation paradigm. As compared to existing works, our framework can handle customers with heterogeneous demands, provide truthfulness as the dominant strategy, enjoy a simple winner determination procedure, and preclude the delayed entrance issue. We also provide the performance analysis of the OBSAs, which is among the first in literature. Regarding the interactions between cloud managers and CPs, we propose two parallel markets for resource gathering, and capture the selfishness of the CPs by their offered prices. We conduct a comprehensive analysis of the two markets and identify the bidding strategies of the cloud managers.}, number={3}, journal={IEEE TRANSACTIONS ON CLOUD COMPUTING}, author={Hosseinalipour, Seyyedali and Dai, Huaiyu}, year={2021}, pages={881–895} }
@article{hosseinalipour_rahmati_eun_dai_2021, title={Energy-Aware Stochastic UAV-Assisted Surveillance}, volume={20}, ISSN={["1558-2248"]}, DOI={10.1109/TWC.2020.3044490}, abstractNote={With the ease of deployment, capabilities of evading the jammers and obscuring their existence, unmanned aerial vehicles (UAVs) are one of the most suitable candidates to perform surveillance. There exists a body of literature in which the inspectors follow a deterministic trajectory to conduct surveillance, which results in a predictable environment for malicious entities. Thus, introducing randomness to the surveillance is of particular interest. In this work, we propose a novel framework for stochastic UAV-assisted surveillance that i) inherently considers the battery constraints of the UAVs, ii) proposes random moving patterns modeled via random walks, and iii) adds another degree of randomness to the system via considering probabilistic inspections. We formulate the problem of interest, i.e., obtaining the energy-efficient random walk and inspection policies of the UAVs subject to probabilistic constraints on inspection criteria of the sites and battery consumption of the UAVs, which turns out to be signomial programming that is highly non-convex. To solve it, we propose a centralized and a distributed algorithm along with their performance guarantee. This work contributes to both UAV-assisted surveillance and classic random walk literature by designing random walks with random inspection policies on weighted graphs with energy limited random walkers.}, number={5}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={Hosseinalipour, Seyyedali and Rahmati, Ali and Eun, Do Young and Dai, Huaiyu}, year={2021}, month={May}, pages={2820–2837} }
@article{su_liwang_hosseinalipour_huang_dai_2021, title={Optimal Position Planning of UAV Relays in UAV-assisted Vehicular Networks}, ISSN={["1550-3607"]}, DOI={10.1109/ICC42927.2021.9500796}, abstractNote={This paper considers unmanned aerial vehicle (UAV)-assisted infrastructure-to-vehicle (I2V) communication employing UAVs as relays to increase the throughput between a roadside unit (RSU) and a vehicular user equipment (VUE). We investigate the UAV position planning problem under both single UAV and multiple cooperative UAVs scenarios while considering the mobility of the VUE, aiming to maximize the data rate of the system. We first consider using a single UAV and prove that the single UAV position planning can be formulated as a convex optimization problem, and then obtain the optimal position of the UAV. Next, we investigate the multiple cooperative UAVs scenario and formulate the joint power control and position planning problem to improve the data rate of the system under a fixed total power consumption. Numerical simulations are provided to verify our theoretical results. Our findings highlight the effects of important system parameters, such as height, transmit power, and the number of UAVs, on the optimal UAV positioning and system performance.}, journal={IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)}, author={Su, Yuhan and LiWang, Minghui and Hosseinalipour, Seyyedali and Huang, Lianfen and Dai, Huaiyu}, year={2021} }
@article{liwang_hosseinalipour_gao_tang_huang_dai_2020, title={Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV}, volume={7}, ISSN={["2327-4662"]}, DOI={10.1109/JIOT.2019.2949602}, abstractNote={Graph jobs represent a wide variety of computation-intensive tasks in which computations are represented by graphs consisting of components (denoting either data sources or data processing) and edges (corresponding to data flows between the components). Recent years have witnessed dramatic growth in smart vehicles and computation-intensive graph jobs, which pose new challenges to the provision of efficient services related to the Internet of Vehicles. Fortunately, vehicular clouds (VCs) formed by a collection of vehicles, which allows jobs to be offloaded among vehicles, can substantially alleviate heavy onboard workloads and enable on-demand provisioning of computational resources. In this article, we present a novel framework for VCs that maps components of graph jobs to service providers via opportunistic vehicle-to-vehicle communication. Then, graph job allocation over VCs is formulated as a nonlinear integer programming with respect to vehicles’ contact duration and available resources, aiming to minimize the job completion time and data exchange cost. The problem is addressed for two scenarios: 1) low-traffic and 2) rush-hour scenarios. For the former, we determine the optimal solutions for the problem. In the latter case, given the intractable computations for deriving feasible allocations, we propose a novel low complexity randomized graph job allocation mechanism by considering hierarchical tree-based subgraph isomorphism extraction. The evaluation of the performance of both optimal and proposed randomized algorithms with two greedy-based baseline methods is carried out through extensive simulations.}, number={1}, journal={IEEE INTERNET OF THINGS JOURNAL}, author={LiWang, M. and Hosseinalipour, Seyyedali and Gao, Zhibin and Tang, Yuliang and Huang, Lianfen and Dai, Huaiyu}, year={2020}, month={Jan}, pages={311–324} }
@article{rahmati_hosseinalipour_yapici_guvenc_dai_bhuyan_2020, title={Energy-Efficient Beamforming and Power Control for Uplink NOMA in mmWave UAV Networks}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM42002.2020.9348114}, abstractNote={The integration of unmanned aerial vehicles (UAVs) into the terrestrial communications networks with a variety of tasks is viewed as a key technology for 5G and beyond. In this work, we consider the uplink millimeter-wave (mmWave) transmission between a set of UAVs and a base station (BS), where the UAVs deploy uplink non-orthogonal multiple access (NOMA) in multiple clusters. Furthermore, the BS also serves its own desired ground user equipment (UE) in the presence of many other ground UEs associated with other cells, which share the same frequency band. Considering the limited energy budget of UAVs, we formulate an energy efficiency (EE) problem, and propose a solution aided by the Dinkelbach's algorithm and successive convex approximation (SCA). Using realistic air-to-ground (A2G) and terrestrial channel models, we assess the performance of the proposed algorithm under various circumstances (maximum transmit power for UAVs, quality-of-service (QoS) constraint for the desired UE, etc.), and identify the best use cases.}, journal={2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)}, author={Rahmati, Ali and Hosseinalipour, Seyyedali and Yapici, Yavuz and Guvenc, Ismail and Dai, Huaiyu and Bhuyan, Arupjyoti}, year={2020} }
@article{hosseinalipour_brinton_aggarwal_dai_chiang_2020, title={From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks}, volume={58}, ISSN={["1558-1896"]}, DOI={10.1109/MCOM.001.2000410}, abstractNote={Machine learning (ML) tasks are becoming ubiquitous in today's network applications. Federated learning has emerged recently as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. There are several challenges with employing conventional federated learning in contemporary networks, due to the significant heterogeneity in compute and communication capabilities that exist across devices. To address this, we advocate a new learning paradigm called fog learning, which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers. Fog learning enhances federated learning along three major dimensions: network, heterogeneity, and proximity. It considers a multi-layer hybrid learning framework consisting of heterogeneous devices with various proximities. It accounts for the topology structures of the local networks among the heterogeneous nodes at each network layer, orchestrating them for collaborative/cooperative learning through device-to-device communications. This migrates from star network topologies used for parameter transfers in federated learning to more distributed topologies at scale. We discuss several open research directions toward realizing fog learning.}, number={12}, journal={IEEE COMMUNICATIONS MAGAZINE}, author={Hosseinalipour, Seyyedali and Brinton, Christopher G. and Aggarwal, Vaneet and Dai, Huaiyu and Chiang, Mung}, year={2020}, month={Dec}, pages={41–47} }
@article{hosseinalipour_nayak_dai_2020, title={Power-Aware Allocation of Graph Jobs in Geo-Distributed Cloud Networks}, volume={31}, ISSN={["1558-2183"]}, DOI={10.1109/TPDS.2019.2943457}, abstractNote={In the era of big-data, the jobs submitted to the clouds exhibit complicated structures represented by graphs, where the nodes denote the sub-tasks each of which can be accommodated at a slot in a server, while the edges indicate the communication constraints among the sub-tasks. We develop a framework for efficient allocation of graph jobs in geo-distributed cloud networks (GDCNs), explicitly considering the power consumption of the datacenters (DCs). We address the following two challenges arising in graph job allocation: i) the allocation problem belongs to NP-hard nonlinear integer programming; ii) the allocation requires solving the NP-complete sub-graph isomorphism problem, which is particularly cumbersome in large-scale GDCNs. We develop a suite of efficient solutions for GDCNs of various scales. For small-scale GDCNs, we propose an analytical approach based on convex programming. For medium-scale GDCNs, we develop a distributed allocation algorithm exploiting the processing power of DCs in parallel. Afterward, we provide a novel low-complexity (decentralized) sub-graph extraction method, based on which we introduce cloud crawlers aiming to extract allocations of good potentials for large-scale GDCNs. Given these suggested strategies, we further investigate strategy selection under both fixed and adaptive DC pricing schemes, and propose an online learning algorithm for each.}, number={4}, journal={IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS}, author={Hosseinalipour, Seyyedali and Nayak, Anuj and Dai, Huaiyu}, year={2020}, month={Apr}, pages={749–765} }
@article{hosseinalipour_mao_eun_dai_2020, title={Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks}, volume={7}, ISSN={["2327-4697"]}, DOI={10.1109/TNSE.2019.2951084}, abstractNote={We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a unique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks. Compared to the existing literature mainly concerned with the node connectivity, we focus on developing effective resource allocation methods to prevent these cascading failures from happening and to mitigate/confine them upon occurrence in the network. To prevent cascading failures, we identify some dangerous stress mechanisms, based on which we quantify the robustness of the network in terms of the resource configuration scheme. Afterward, we identify the optimal resource configuration under two resource/load fluctuations scenarios: uniform and proportional fluctuations. We further investigate the optimal resource configuration problem considering heterogeneous resource sharing costs among the nodes. To mitigate/confine ongoing cascading failures, we propose two network adaptations mechanisms: intentional failure and resource re-adjustment, based on which we propose an algorithm to mitigate an ongoing cascading failure while reinforcing the surviving network with a high robustness to avoid further failures.}, number={3}, journal={IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING}, author={Hosseinalipour, Seyyedali and Mao, Jiayu and Eun, Do Young and Dai, Huaiyu}, year={2020}, pages={1710–1723} }
@article{chattopadhyay_dai_eun_hosseinalipour_2017, title={Designing Optimal Interlink Patterns to Maximize Robustness of Interdependent Networks Against Cascading Failures}, volume={65}, ISSN={["1558-0857"]}, DOI={10.1109/tcomm.2017.2709302}, abstractNote={In this paper, we consider the optimal design of interlinks for an interdependent system of networks. In contrast to existing literature, we explicitly exploit the information of intra-layer node degrees to design interdependent structures such that their robustness against cascading failures, triggered by randomized attacks, is maximized. Utilizing percolation theory-based system equations relating the robustness of the network to its degree sequence, we characterize the optimal design for the one-to-one structure, with complete interdependence and partial interdependence, under randomized attack. We also extend our study to the one-to-many interdependence structure and the targeted attack model. The theoretically derived optimal interdependence structures have been verified using simulations on scale-free networks.}, number={9}, journal={IEEE TRANSACTIONS ON COMMUNICATIONS}, author={Chattopadhyay, Srinjoy and Dai, Huaiyu and Eun, Do Young and Hosseinalipour, Seyyedali}, year={2017}, month={Sep}, pages={3847–3862} }
@inproceedings{hosseinalipour_wang_dai_wang_2017, title={Detection of infections using graph signal processing in heterogeneous networks}, DOI={10.1109/glocom.2017.8254487}, abstractNote={Determining the causality of abnormalities in a network is the prerequisite for developing countermeasures. In this paper, we focus on infection detection in heterogeneous networks. Given a snapshot of the network which demonstrates the condition of the nodes, the goal is to distinguish between random failures and epidemic scenarios. We model the network situation as a graph signal based on the nodes' status. Detection metrics motivated by graph signal processing are introduced for the infection detection problem in hand, and an effective algorithm is proposed to solve it. Simulation results indicate a dramatic improvement in terms of detection probability compared to the current state-of-the-art.}, booktitle={Globecom 2017 - 2017 ieee global communications conference}, author={Hosseinalipour, S. and Wang, Jie and Dai, Huaiyu and Wang, Wenye}, year={2017} }
@inproceedings{nayak_hosseinalipour_dai_2017, title={Dynamic advertising in VANETs using repeated auctions}, DOI={10.1109/glocom.2017.8254662}, abstractNote={Vehicular ad-hoc networks (VANETs) have been an active area in the research community during the last decade with focus primarily on routing protocols, security aspects and safety. Recent advances in wireless communication and the inherent dynamic nature of VANETs provide excellent opportunity for advertisement dissemination. In this paper, we address the problem of dynamic advertising in VANETs. We consider a city divided into a grid, where the blocks have different vehicular densities that vary over time. Several advertising companies compete for the blocks to broadcast their advertisements in the network. The content dissemination in the network is controlled by a data management unit that receives requests from advertising companies for each block. To solve the problem of block allocation, we adapt the repeated auction scheme for the dynamic setting. Two new metrics are defined to better represent the real- world scenario and fairness in repeated auctions. We propose an algorithm which is a combination of adaptive linear prediction and nonparametric Bayesian belief update, enabling smart bidding and improving the utilities of the competing advertising companies significantly in the long- run. Through simulations, we show that the proposed algorithm achieves better performance than two baselines approaches.}, booktitle={Globecom 2017 - 2017 ieee global communications conference}, author={Nayak, A. and Hosseinalipour, S. and Dai, H. Y.}, year={2017} }
@inproceedings{hosseinalipour_dai_2017, title={Options-based sequential auctions for dnamic cloud resource allocation}, DOI={10.1109/icc.2017.7997242}, abstractNote={With growing demands for cloud computing services, the idea of managing limited cloud resources for making a profit has arisen as an important problem. Auction theory is recently considered as a viable way to solve the problem of cloud resource allocation. In this paper, we consider a model for Cloud of Clouds Networks (CCNs) with different types of servers along with customers with heterogeneous demands, in which customers and cloud servers may join and leave the CCN at will. We propose an options-based sequential auction that not only provides a good match with the dynamic structure of the problem, but also solves the entrance time problem and possesses the truthfulness property. We study both first-price and second-price options-based sequential auctions, and model the price matching processes in those auctions as Markov chains. We provide mathematically tractable methods to find the expected value of the CCN manager's revenue, and further show how the proxy agents' patience time affects the CCN manager's revenue.}, booktitle={2017 ieee international conference on communications (icc)}, author={Hosseinalipour, S. and Dai, H. Y.}, year={2017} }
@inproceedings{hosseinalipour_nayak_dai_2017, title={Real-time strategy selection for mobile advertising in VANETs}, DOI={10.1109/glocom.2017.8253975}, abstractNote={Vehicular ad-hoc networks (VANETs) has recently attracted a lot of attention due to their great potentials for different applications such as collision avoidance, route finding and autonomous driving. A wide range of coverage and accessibility to the end users in VANETs make them a good target for commercial advertising. This paper addresses the problem of mobile advertising in VANETs. We consider a case where different advertisers compete for the VANET infrastructure. It is assumed that a city is partitioned into a grid of blocks and the central data center manager (CDM) sets the rental price for each block considering the geographical position and the predicted density of vehicles inside the block. The regret-based minimization method is adopted to tackle the problem in response to its dynamic nature. Regret bound of the proposed algorithm and its convergence to the best strategy are shown rigorously. Furthermore, a good potential of the proposed algorithm is revealed through simulations.}, booktitle={Globecom 2017 - 2017 ieee global communications conference}, author={Hosseinalipour, S. and Nayak, A. and Dai, H. Y.}, year={2017} }