Syed Ali Raza Zaidi

Secure D2D Communication in Large-Scale Cognitive Cellular Networks: A Wireless Power Transfer Model

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multiantenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the primary base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, three wireless power transfer (WPT) policies are proposed: 1) co-operative power beacons (CPB) power transfer, 2) best power beacon (BPB) power transfer, and 3) nearest power beacon (NPB) power transfer. To characterize the power transfer reliability of the proposed three policies, we derive new expressions for the exact power outage probability. Moreover, the analysis of the power outage probability is extended to the case when PBs are equipped with large antenna arrays. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), where the receiver with the strongest channel is selected; and 2) nearest receiver selection (NRS), where the nearest receiver is selected. To assess the secrecy performance, we derive new analytical expressions for the secrecy outage probability and the secrecy throughput considering the two receiver selection schemes using the proposed WPT policies. We presented Monte carlo simulation results to corroborate our analysis and show: 1) secrecy performance improves with increasing densities of PBs and D2D receivers due to larger multiuser diversity gain; 2) CPB achieves better secrecy performance than BPB and NPB but consumes more power; and 3) BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead. A pivotal conclusion is reached that with increasing number of antennas at PBs, NPB offers a comparable secrecy performance to that of BPB but with a lower complexity.

Deep learning approach for Network Intrusion Detection in Software Defined Networking

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

Achievable Spatial Throughput in Multi-Antenna Cognitive Underlay Networks with Multi-Hop Relaying

In this article, we quantify the achievable spatial throughput of a multi-antenna Poisson cognitive radio network (CRN) collocated with a Poisson multi-antenna primary network. CR users employ Slotted-ALOHA medium access control. The success probability (SP) of a primary link is quantified in the presence of the secondary and primary interferers. It is demonstrated that two fold gains are experienced by employing multiple antennas at primary, i.e., (i) the fixed high desired SP threshold is met; (ii) CRs can also be accommodated without QoS deterioration. Further in this paper, the maximum permissible medium access probability (MAP) for CRN is derived from the link SP and primary users QoS constraint. The impact of the number of antennas and modulation employed at the primary on the permissible MAP of the CRN is also explored. Assuming that CR users employ multi-hop communication, QoS aware relaying with a radian sector forwarding area is studied. The average forward progress (AFP) and isolation probability for a CR user with QoS based connectivity is characterized under the permissible MAP. The spatial throughput for the CRN is quantified by the analysis of the AFP and the permissible MAP. It is shown that there exists an optimal MAP which maximizes the spatial throughput of the CRN. This optimal MAP is coupled with the permissible MAP, density of users, number of antennas and modulation schemes employed in both primary and secondary networks. Lastly, a few important design questions are investigated for multi-hop MIMO underlay CRNs.

On minimum cost coverage in wireless sensor networks

A solution to the coverage problem in wireless sensor networks provides the total number of sensors that are required to cover a given area of deployment. While prior studies have proposed different formulations and solutions to this problem, these studies have not addressed the problem of minimum cost coverage in which full coverage is achieved by using the minimum number of sensor nodes for an arbitrary geometric shape region. In this paper, we present a geometric solution to the minimum cost coverage problem under a deterministic deployment. Furthermore, we present a probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region. We demonstrate that for virtually 100% probability of coverage, random deployment needs approximately seven times more sensors as compared to a deterministic setup.

Outage probability analysis of cognitive radio networks under self-coexistence constraint

Cognitive radio networks (CRNs) are envisioned to eradicate the artificial scarcity caused by todays stringent spectrum allocation policy. In this article, we develop a statistical framework to model the outage probability at any arbitrary primary/licensed user, while operating in the presence of a collocated secondary network/CRN. A system model based on stochastic geometry (utilizing the theory of a Poisson point process) is introduced to model the transmission/reception/detection uncertainty due to the random locations and topology of both primary and secondary users. The primary beacon enabled interweave spectrum sharing model is utilized for evaluating outage and interference at a typical primary receiver. It is shown that the self-coexistence constraint ignored in past studies plays a vital role in the determination of outage and interference. A statistical model for interference is developed to incorporate the self-coexistence constraint in terms of medium access probability (MAP). Our study also indicates that under the availability of multiple channels/sub-channels, outage probability also depends on the probability of picking the same channel. Optimal selection of MAP and channel selection probability is briefly discussed. Our analytical and simulation results further consolidate the argument that past studies which do not cater for the self-coexistence constraint, over-estimate the interference.

Breaking the Area Spectral Efficiency Wall in Cognitive Underlay Networks

In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network. The developed framework explicitly accommodates channel, topological and medium access uncertainties. The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks. To this end, we highlight two available degrees of freedom, i.e., shaping medium access or transmit power. While from the primary users perspective tuning either to control the interference is equivalent, the picture is different for the secondary network. We show the existence of an area spectral efficiency wall under both adaptation schemes. We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance. But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks. We explore several design parameters for both adaptation schemes. Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies.

On the security of large scale spectrum sharing networks

We investigate beamforming and artificial noise generation at the secondary transmitters to establish secure transmission in large scale spectrum sharing networks, where multiple non-colluding eavesdroppers attempt to intercept the secondary transmission. We develop a comprehensive analytical framework to accurately assess the secrecy performance under the primary users quality of service constraint. Our aim is to characterize the impact of beamforming and artificial noise generation on this complex large scale network. We first derive the exact expressions for the average secrecy rate and the secrecy outage probability. Our results show that there exists an average secrecy rate wall beyond which the primary users quality of service is violated. Interestingly, we find that different from the conventional network with fixed nodes where equal power allocation achieves near optimal average secrecy rate, the equal power allocation may not be a good option for large scale spectrum sharing networks.

Secure D2D communication in large-scale cognitive cellular networks with wireless power transfer

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multi-antenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the cellular base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, we propose a new power transfer policy, namely, best power beacon (BPB) power transfer. To characterize the power transfer reliability of the proposed policy, we derive new closed-form expressions for the exact power outage probability and the asymptotic power outage probability with large antenna arrays at PBs. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), and 2) nearest receiver selection (NRS). To assess the secrecy performance, we derive new expressions for the secrecy throughput considering the two receiver selection schemes using the BPB power transfer policies. We show that secrecy performance improves with increasing densities of PBs and D2D receivers because of a larger multiuser diversity gain. A pivotal conclusion is reached that BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead.

Drone Empowered Small Cellular Disaster Recovery Networks for Resilient Smart Cities

Resilient communication networks, which can continue operations even after a calamity, will be a central feature of future smart cities. Recent proliferation of drones propelled by the availability of cheap commodity hardware presents a new avenue for provisioning such networks. In particular, with the advent of Googles Sky Bender and Facebooks internet drone, drone empowered small cellular networks (DSCNs) are no longer fantasy. DSCNs are attractive solution for public safety networks because of swift deployment capability and intrinsic network reconfigurability. While DSCNs have received some attention in the recent past, the design space of such networks has not been extensively traversed. In particular, co-existence of such networks with an operational ground cellular network in a post-disaster situation has not been investigated. Moreover, design parameters such as optimal altitude and number of drone base stations, etc., as a function of destroyed base stations, propagation conditions, etc., have not been explored. In order to address these design issues, we present a comprehensive statistical framework which is developed from stochastic geometric perspective. We then employ the developed framework to investigate the impact of several parametric variations on the performance of the DSCNs. Without loss of any generality, in this article, the performance metric employed is coverage probability of a down-link mobile user. It is demonstrated that by intelligently selecting the number of drones and their corresponding altitudes, ground users coverage can be significantly enhanced. This is attained without incurring significant performance penalty to the mobile users which continue to be served from operating ground infrastructure.

Characterizing Coverage and Downlink Throughput of Cloud Empowered HetNets

In this letter, we introduce the concept of cloud empowered heterogeneous networks. We propose a simple yet efficient association mechanism for the selection of a serving remote radio head (RRH) for a desired mobile user (MU). We introduce the concept of user centric clustering which improves the user throughput by contributing on several fronts. Employing well established tools from stochastic geometry, we characterize the downlink coverage probability for the MU. The coverage probability is in turn employed to characterize the attainable throughput at a certain desired reliability constraint. Simulation results are presented to indicate the gains experienced by employing additional tiers in a cloud radio access network(C-RAN). Such gains are attributed to the distributed diversity which provides a power gain and hence increases the effective received SNR.