MEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

Fast Filtering of Search Results Sorted by Attribute

Modern search services often provide multiple options to rank the search results, e.g., sort “by relevance”, “by price” or “by discount” in e-commerce. While the traditional rank by relevance effectively places the relevant results in the top positions of the results list, the rank by attribute could place many marginally relevant results in the head of the results list leading to poor user experience. In the past, this issue has been addressed by investigating the relevance-aware filtering problem, which asks to select the subset of results maximizing the relevance of the attribute-sorted list. Recently, an exact algorithm has been proposed to solve this problem optimally. However, the high computational cost of the algorithm makes it impractical for the Web search scenario, which is characterized by huge lists of results and strict time constraints. For this reason, the problem is often solved using efficient yet inaccurate heuristic algorithms. In this article, we first prove the performance bounds of the existing heuristics. We then propose two efficient and effective algorithms to solve the relevance-aware filtering problem. First, we propose OPT-Filtering, a novel exact algorithm that is faster than the existing state-of-the-art optimal algorithm. Second, we propose an approximate and even more efficient algorithm, ϵ-Filtering, which, given an allowed approximation error ϵ, finds a (1-ϵ)–optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of the two proposed algorithms against state-of-the-art competitors on two real-world public datasets. Experimental results show that OPT-Filtering achieves a significant speedup of up to two orders of magnitude with respect to the existing optimal solution, while ϵ-Filtering further improves this result by trading effectiveness for efficiency. In particular, experiments show that ϵ-Filtering can achieve quasi-optimal solutions while being faster than all state-of-the-art competitors in most of the tested configurations.

Online Resource Provisioning for Wireless Data Collection

Wireless data collection requires a sequence of resource provisioning decisions due to the limited battery capacity of wireless sensors. The corresponding online resource provisioning problem is challenging. Recently, many prediction methods have been proposed that can be used to benefit the performance of various systems through their incorporation. Therefore, in this article, we focus on online resource provisioning problems with short-term predictions motivated by the wireless data collection problem. Specifically, we design separate online algorithms for systems in which the state evolves in either a stationary manner or an arbitrarily determined manner and prove their performance bounds where their bounds improve as the amount of available predictions increases. Additionally, we design a meta-algorithm that can choose which online algorithm to implement at each point in time, depending on the recent behavior of the system environment. The practical performances of the proposed algorithms are corroborated in trace-driven numerical simulations of data collection of shared bikes. Additionally, we show that the performance of our meta-algorithm in various system environments can be better than that of the single best algorithm chosen in hindsight.

Variable Gain Prescribed Performance Control for Dynamic Positioning of Ships with Positioning Error Constraints

In this paper, a variable gain prescribed performance control law is proposed for dynamic positioning (DP) of ships with positioning error constraints, input saturation and unknown external disturbances. The error performance index functions are designed to preset the prescribed performance bounds and the error mapping functions are constructed to incorporate the prescribed performance bounds into the DP control design. The variable gain technique is used to limit the output amplitude of the control law to avoid input saturation of the system by dynamically adjusting the control gain of the DP control law according to the positioning errors, and the error mapping function replaces the positioning error as a recursive sliding-mode surface to realize the prescribed performance control of the system and guarantee the stability of the closed-loop system with variable control gains. It has been proved that the proposed DP control law can make the uniformly ultimately boundedness of all signals in the DP closed-loop control system. The numerical simulation results illustrate that the proposed control law can make the ship’s position and heading maintain at the desired value with positioning error constraints, enhance the non-fragility of the DP control law to the perturbation of system’s parameters and improve the system’s rejection ability to external disturbances.

Low-dissipation model of three-terminal refrigerator: Performance bounds and comparative analyses

In the present paper, a general non-combined model of three-terminal refrigerator is established based on the low-dissipation assumption. The relation between the optimized cooling power and the corresponding coefficient of performance (COP) is analytically derived, according to which the COP at maximum cooling power (CMP) can be further determined. At two dissipation asymmetry limits, upper and lower bounds of CMP are obtained and found to be in good agreement with experimental and simulated results. Additionally, comparison of the obtained bounds with previous combined model is presented. In particular it is found that the upper bounds are the same, whereas the lower bounds are quite different. This feature indicates that the claimed universal equivalence for the combined and non-combined models under endoreversible assumption is invalid within the frame of low-dissipation assumption. Then, the equivalence between various finite-time thermodynamic models needs to be reevaluated regarding multi-terminal systems. Moreover, the correlation between the combined and non-combined models is further revealed by the derivation of the equivalent condition according to which the identical upper bounds and distinct lower bounds are theoretically shown. Finally, the proposed non-combined model is proved to be the appropriate model for describing various types of thermally driven refrigerator. This work may provide some instructive information for the further establishments and performance analyses of multi-terminal low-dissipation models.

Dynamic Programs with Shared Resources and Signals: Dynamic Fluid Policies and Asymptotic Optimality

Allocating Resources Across Systems Coupled by Shared Information Many sequential decision problems involve repeatedly allocating a limited resource across subsystems that are jointly affected by randomly evolving exogenous factors. For example, in adaptive clinical trials, a decision maker needs to allocate patients to treatments in an effort to learn about the efficacy of treatments, but the number of available patients may vary randomly over time. In capital budgeting problems, firms may allocate resources to conduct R&D on new products, but funding budgets may evolve randomly. In many inventory management problems, firms need to allocate limited production capacity to satisfy uncertain demands at multiple locations, and these demands may be correlated due to vagaries in shared market conditions. In this paper, we develop a model involving “shared resources and signals” that captures these and potentially many other applications. The framework is naturally described as a stochastic dynamic program, but this problem is quite difficult to solve. We develop an approximation method based on a “dynamic fluid relaxation”: in this approximation, the subsystem state evolution is approximated by a deterministic fluid model, but the exogenous states (the signals) retain their stochastic evolution. We develop an algorithm for solving the dynamic fluid relaxation. We analyze the corresponding feasible policies and performance bounds from the dynamic fluid relaxation and show that these are asymptotically optimal as the number of subsystems grows large. We show that competing state-of-the-art approaches used in the literature on weakly coupled dynamic programs in general fail to provide asymptotic optimality. Finally, we illustrate the approach on the aforementioned dynamic capital budgeting and multilocation inventory management problems.

On the accuracy of emitter localization method based on multipath exploitation in realistic scenarios

This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures (ESM) systems. Previously, only the theoretical development of the localization method was targeted by the authors. In fact, this could be a serious concern in practice since there is no evidence about its accuracy under the real data gathered from realistic scenarios. Therefore, firstly, an accurate ray-tracing algorithm is adapted to the method in order to enable its implementation in practice. Then, scenarios are determined based on the geographic information system (GIS) map generated to collect high resolution digital terrain elevation data (DTED) as well as realistic localization problems for radar emitters. Next, the improved method is tested with simulations, and thus, its performance is verified for practical implementation in Electronic Warfare (EW) context for the first time in the literature. Lastly, based on the simulation results, the performance bounds of the method are also discussed.

Channel Prediction for Mobile MIMO Wireless Communication Systems

<p>Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes.</p>

Channel Prediction for Mobile MIMO Wireless Communication Systems

<p>Temporal variation and frequency selectivity of wireless channels constitute a major drawback to the attainment of high gains in capacity and reliability offered by multiple antennas at the transmitter and receiver of a mobile communication system. Limited feedback and adaptive transmission schemes such as adaptive modulation and coding, antenna selection, power allocation and scheduling have the potential to provide the platform of attaining the high transmission rate, capacity and QoS requirements in current and future wireless communication systems. Theses schemes require both the transmitter and receiver to have accurate knowledge of Channel State Information (CSI). In Time Division Duplex (TDD) systems, CSI at the transmitter can be obtained using channel reciprocity. In Frequency Division Duplex (FDD) systems, however, CSI is typically estimated at the receiver and fed back to the transmitter via a low-rate feedback link. Due to the inherent time delays in estimation, processing and feedback, the CSI obtained from the receiver may become outdated before its actual usage at the transmitter. This results in significant performance loss, especially in high mobility environments. There is therefore a need to extrapolate the varying channel into the future, far enough to account for the delay and mitigate the performance degradation. The research in this thesis investigates parametric modeling and prediction of mobile MIMO channels for both narrowband and wideband systems. The focus is on schemes that utilize the additional spatial information offered by multiple sampling of the wave-field in multi-antenna systems to aid channel prediction. The research has led to the development of several algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient methods for the extrapolation of narrowband MIMO channels are proposed. Various extensions were also developed. These include methods for wideband channels, transmission using polarized antenna arrays, and mobile-to-mobile systems. Performance bounds on the estimation and prediction error are vital when evaluating channel estimation and prediction schemes. For this purpose, analytical expressions for bound on the estimation and prediction of polarized and non-polarized MIMO channels are derived. Using the vector formulation of the Cramer Rao bound for function of parameters, readily interpretable closed-form expressions for the prediction error bounds were found for cases with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The derived performance bounds are very simple and so provide insight into system design. The performance of the proposed algorithms was evaluated using standardized channel models. The effects of the temporal variation of multipath parameters on prediction is studied and methods for jointly tracking the channel parameters are developed. The algorithms presented can be utilized to enhance the performance of limited feedback and adaptive MIMO transmission schemes.</p>