Mo Ghorbanzadeh

A Utility Proportional Fairness Resource Allocation in Spectrally Radar-Coexistent Cellular Networks

Spectrum sharing is an elegant solution to addressing the scarcity of the bandwidth for wireless communications systems. This research studies the feasibility of sharing the spectrum between sectorized cellular systems and stationary radars interfering with certain sectors of the communications infrastructure. It also explores allocating optimal resources to mobile devices in order to provide with the quality of service for all running applications whilst growing the communications network spectrally coexistent with the radar systems. The rate allocation problem is formulated as two convex optimizations, where the radar-interfering sector assignments are extracted from the portion of the spectrum non-overlapping with the radar operating frequency. Such a double-stage resource allocation procedure inherits the fairness into the rate allocation scheme by first assigning the spectrally radar-overlapping resources.

A neural network approach to category validation of Android applications

Permission structure of Android applications introduces security vulnerabilities which can be readily exploited by third-party applications. We address certain exploitability aspects by means of neural networks, effective classification techniques capable of verifying the application categories. We devise a novel methodology to verify an application category by machine-learning the application permissions and estimating likelihoods of the extant categories. The performance of our classifier is optimized through the joint minimization of false positive and negative rates. Applying our modus operandi to 1,700 popular third-party Android applications and malwares, a major portion of the category declarations were judged truthfully. This manifests effectiveness of neural network decision engines in validating Android application categories.

A utility proportional fairness radio resource block allocation in cellular networks

This paper presents a radio resource block allocation optimization problem for cellular communications systems with users running delay-tolerant and real-time applications, generating elastic and inelastic traffic on the network and being modelled as logarithmic and sigmoidal utilities respectively. The optimization is cast under a utility proportional fairness framework aiming at maximizing the cellular systems utility whilst allocating users the resource blocks with an eye on application quality of service requirements and on the procedural temporal and computational efficiency. Ultimately, the sensitivity of the proposed modus operandi to the resource variations is investigated.

A hidden Markov model detection of malicious Android applications at runtime

A hidden Markov model approach is leveraged to detect potentially malicious Android applications at runtime based on analyzing the Intents passing through the binder. Real world applications are emulated, their Intents are parsed, and, after appropriate discretization of the Intent action fields, they train the hidden Markov models for recognizing anomalous and benign Android application behaviors. The inferred stochastic processes can probabilistically estimate whether an application is performing a malicious or benign action as it is running on the device. Such a decision is realized through a maximum likelihood estimation. The results show that the method is capable of detecting malicious Android applications as they run on the platform.

Fine-grained end-to-end network model via vector quantization and hidden Markov processes

We study and compare modeling an end-to-end network by conventional, bivariate, and exponential observation hidden Markov processes. Furthermore, effects of μ-law, Lindle-Boyde-Gray, and uniform quantization approaches on the modeling granularity is explored. We performed experiments using synthetic representative data from a traffic-modeler autoregressive modular process and the Network Simulator software as well as over-the-Internet experiments with real data to contrast the fidelity produced from each model. Comparing statistical signatures of the model-generated data with those of the training sequence indicates that accompanying Lindle-Boyde-Gray quantization with conventional or bivariate hidden Markov processes significantly improves the modeling fidelity.

Distributed Resource Allocation

In Chap. 3, we introduced a novel convex utility proportional fairness maximization for optimal resource allocation in wireless networks and outfitted the optimization with the subscriber, application status, and service differentiations parameterized, respectively, as UE subscription weights, application status weights, and application utility functions. Furthermore, we developed a centralized architecture for the proposed resource allocation which assigned application rates by the eNBs in a single stage in response to the application utility parameters sent by the UEs to the eNBs. In this chapter, we provide with a distributed architecture for the same radio resource allocation framework which was introduced in Chap. 3. The resource allocation optimization accounts for application types and temporal usages as well as UE priorities.

Radar interference into LTE base stations in the 3.5 GHz band

We study the interference from a rotating shipborne radar system that spectrally and spatially coexists with a Long Term Evolution (LTE) cellular communications network in the 3.5 GHz band to investigate the feasibility of LTE deployment in the United States coastal metropolitan cities in that band. First, we simulate the radar systems with realistic operational parameters. Furthermore, we leverage a detailed 3GPP-compliant LTE simulation with a sophisticated air interface modeling and investigate sensitivity of LTE to radar interference in macro cell, outdoor small cell, and indoor small cell scenarios. We simulate the propagation conditions between the radar and LTE system by adopting the Free Space Path Loss and Irregular Terrain Model commonly leveraged by National Telecommunications and Information Administration (NTIA), to account for propagation, diffraction, and troposcatter losses that the radar pulses undergo before they reach the LTE system. As a performance metric, we evaluate the throughput of the LTE system in the uplink direction for various distances between the radar and the cellular system. Our simulation results indicate an LTE link will remain operational even in severe interference conditions. In fact, the LTE system as close as 100 km away from the radar undergoes less than 10 % throughput loss from the LTE total throughput, and the throughput loss is less than 30 % when the radar is only 50 km away from the LTE.

Delay-Based Backhaul Modeling

Network simulation, for the purpose of application performance evaluation, using event-driven simulators such as Network Simulator (NS2) [2], Dummynet [3], and NIST [4] Net requires such a multitude of component settings, which makes large-scale networks inconducive to simulation. This has inspired developing simple generic models for networks, regardless of their size and complexity, by means of hidden Markov processes (HMPs) [5] Salamatian et al. [6] use conventional HMPs (CHMPs) [7] for packet loss modeling. Weiwei et al. [1, 5] deploy bivariate HMPs (BHMPs) for delay-based network modeling in that end-to-end delay drastically affects the performance of applications running on the network. Therefore, designing network models based on such delays is insightful to applications performance as HMP-generated observations represent actual delays the running applications will undergo. The authors in [8] used HMPs for security of Android applications at runtime, and [9] leveraged them for install-time security checks. Since HMP parameters are the only means of generating the delays, selecting an HMP variation and its precise inference is of high consequence.

Resource Allocation Architectures Traffic and Sensitivity Analysis

Resource allocation methods which aim at fulfilling the QoS requirements of current-day cellular networks have been the focus of many research studies. This attention to QoS-minded resource allocation techniques is partly because present-day cellular communication systems contain smart phones capable of running several applications simultaneously. Since the applications have miscellaneous QoS requirements based on the type of the traffic that they deal with, many modern resource allocation schemes incorporate the application QoS requirements into their operation. Furthermore, a wide variety of subscribers use the current communication systems; as such subscriber types have been included in many of the current resource allocation techniques. Besides, concurrent running of the applications on the smart phones motivates including the application usage temporal changes into novel resource allocation schemes. Much of the state of the art in single carrier resource allocation [1, 2, 3, 4] considers either the traffic nature or the subscriber type; However, these works do not address whether the allocated rates are optimal and do not account for all the aforementioned QoS-related issues simultaneously.

Radio Resource Block Allocation

This chapter leverages the proportional fairness resource allocation that we presented in Chap. 3 and expands it to a Resource Block (RB) allocation for cellular communications networks, where the UEs consists of delay-tolerant and real-time applications generating inelastic and elastic data traffic.