Shared Wireless Channel Research Articles

Allocation of Eavesdropping Attacks for Multi-System Remote State Estimation

In recent years, the problem of cyber–physical systems’ remote state estimations under eavesdropping attacks have been a source of concern. Aiming at the existence of eavesdroppers in multi-system CPSs, the optimal attack energy allocation problem based on a SINR (signal-to-noise ratio) remote state estimation is studied. Assume that there are N sensors, and these sensors use a shared wireless communication channel to send their state measurements to the remote estimator. Due to the limited power, eavesdroppers can only attack M channels out of N channels at most. Our goal is to use the Markov decision processes (MDP) method to maximize the eavesdropper’s state estimation error, so as to determine the eavesdropper’s optimal attack allocation. We propose a backward induction algorithm which uses MDP to obtain the optimal attack energy allocation strategy. Compared with the traditional induction algorithm, this algorithm has lower computational cost. Finally, the numerical simulation results verify the correctness of the theoretical analysis.

Remote Tracking of Distributed Dynamic Sources Over a Random Access Channel With One-Bit Updates

In this work, we consider a network, where distributed information sources whose states evolve according to a random process transmit their time-varying states to a remote estimator over a shared wireless channel. Each source generates packets in a decentralized manner and employs a slotted random access mechanism to transmit the packets. In particular, we are interested in networks with a large number of low-complexity devices that share low-capacity random access channels. Accordingly, we investigate update strategies for remote tracking of source states that require each update to constitute as few bits as possible. To that end, we develop update strategies requiring only one-bit of information per update that employ a local cancellation strategy. We further analytically compare the performance of the cancellation-enabled update policy to the optimal policy that does not restrict the number of bits for each update, which show that an asymptotic upper bound of the optimality ratio is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{13\sqrt{2}}{12}$</tex-math></inline-formula> . Through simulations, we compare the proposed cancellation-enabled one-bit update policy with zero-wait sampling and threshold-based sampling policies that require more than one-bit of information per update. The comparisons show that the cancellation-enabled update policy at its optimal threshold level outperforms the multi-bit update policies.

Submodularity-based sensor scheduling for state estimation among multiple linear dynamical systems

In this paper we focus on the sensor scheduling for dynamic state estimation among multiple processes. Multiple sensors transmit their measurements to the remote estimator via a shared wireless channel. Only a subset of sensors are allowed to transmit at each time. The objective of the scheduler is to maximize a linear combination of the terminal time estimation error covariances in a log-determinant sense. We develop conditions under which the objective is monotone and submodular. A submodularity-based greedy scheduling algorithm is proposed, and its performance bound is provided. Numerical examples are presented to illustrate the theoretical findings.

Data-Based Distributed Sensor Scheduling for Multiple Linear Systems With $H_\infty$ Performance Preservation

This article investigates the data-based distributed sensor scheduling for a wireless sensor network (WSN), where multiple sensor nodes monitor different linear systems correspondingly. The WSN admits a network topology to formulate a fully distributed sensor scheduling policy, and transmits measured information over a shared wireless channel. Due to the bandwidth limit, at each time only partial sensor nodes can send their measurements to the corresponding remote controller. By introducing a distributed minimum subset extraction mechanism under Q-learning framework, a data-based sensor scheduling algorithm is proposed, which gives an approximate solution to minimizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance index of the overall closed-loop system, without requiring the knowledge of system parameters. Also, under persistently exciting condition with sufficiently rich enough disturbances, the algorithm converges to the exact optimal solution. The effectiveness of the proposed algorithm is demonstrated with simulation results.

Data-Driven Random Access Optimization in Multi-Cell IoT Networks Using NOMA

Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks, where IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol. To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity. It is shown that the network optimization objective is high dimensional and mathematically intractable, yet it admits favourable mathematical properties that enable the design of efficient data-driven algorithmic solutions which do not require a priori knowledge of the channel model or network topology. A centralized model-based algorithm and a scalable distributed model-free algorithm, are proposed to optimally tune the transmission probabilities of IoT devices and attain the maximum capacity. The convergence of the proposed algorithms to the optimal solution is further established based on convex optimization and game-theoretic analysis. Extensive simulations demonstrate the merits of the novel formulation and the efficacy of the proposed algorithms.

Federated Learning in Multi-RIS-Aided Systems

The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.

Cross-Layer Analysis of Multi-Static RFID Systems Exploiting Capture Diversity

In this paper, we propose a framework for cross-layer analysis of multi-static passive RFID systems. The model takes into account details of the shared wireless channel, including fading and capture effect, whereas, at the medium access control (MAC) layer, the anti-collision mechanism proposed in the EPC Generation 2 standard is taken as a reference. To address the complexity of the system model, we rely on a semi-analytical approach, that combines a moment matching approximation method to abstract the physical layer and Monte-Carlo simulations to describe the MAC dynamics. Furthermore, based on the space diversity feature offered by the multi-static settings, we introduce the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">capture diversity</i> and propose a modification to the standard to fully support this form of diversity. Numerical results show the impact of deployment conditions and the relative positions of interrogator, tags, and detection points on the performance of tags’ identification. We show how the number of detection points impacts the system performance under various channel conditions and MAC parameters’ settings. Finally, we validate the proposed update of the MAC protocol, showing substantial performance improvement with respect to the standard collision resolution policy.

Exploiting Capture Diversity for Performance Enhancement of ALOHA-Based Multi-Static Backscattering Systems in the 6G Perspective.

In this paper, we consider the emerging context of ALOHA-based multi-static backscattering communication systems. By assuming an architecture consisting of a set of passive backscattering nodes, an illuminator, and a set of spatially dislocated receivers, we firstly propose a cross-layer framework for performance analysis. The model jointly accounts for the shared wireless channel, including fading and capture effect, and channel contention strategy, which is regulated by a Framed Slotted ALOHA protocol. Furthermore, based on the inherent macroscopic diversity offered by the multi-static settings, we introduce the concept of capture diversity, which is shown to enable multiple packet detection in slots with multiple transmissions. In order to characterize the multiple access interference and approximate the capture probabilities, we enforce a log-normal approximation of the inverse Signal-to-Interference Ratio that relies on moment matching. Numerical results show the impact of deployment scenarios and the relative positions of illuminator, backscattering nodes, and receivers on the system normalized throughput. We show how the number of detection points impacts the system performance under various channel conditions. Moreover, the accuracy of the proposed approximation rationale is validated via Monte Carlo simulations. Finally, we analyze the optimal frame length in the presence of capture diversity.

Joint design of control policy and network scheduling policy for wireless networked control systems: Theory and application

In this paper, we study a wireless networked control system (WNCS) with N⩾2 sub-systems sharing a common wireless channel. Each sub-system consists of a plant and a controller and the control message must be delivered from the controller to the plant through the shared wireless channel. The wireless channel is unreliable due to shadowing and fading. As a result, a packet can be successfully delivered in a slot with a certain probability. A network scheduling policy determines how to transmit those control messages generated by such N sub-systems and directly influences the transmission delay of control messages. We first consider the case that all sub-systems have the same sampling period. We characterize the stability condition of such a WNCS under the joint design of the control policy and the network scheduling policy by means of 2N linear inequalities. For scalar systems, we further simplify the stability condition into only one linear inequality for two special cases: the perfect-channel case and the symmetric-structure case. One main technical contribution of this paper is to introduce the recent results on the network scheduling policy design for delay-constrained wireless communications into the analysis of WNCSs. In addition, we have applied our theory to a practical problem of stabilizing multiple pendulum-cart sub-systems over a shared wireless channel. Simulations show that our joint design effectively achieves better performance than existing baseline.

Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity. We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round, and how the resources should be allocated among the participating devices, not only based on their channel conditions, but also on the significance of their local model updates. We then establish convergence of a wireless FL algorithm with device scheduling, where devices have limited capacity to convey their messages. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually. Furthermore, we observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., scheduling multiple devices at each round improves the performance. This observation is verified by the convergence result, which shows that the number of scheduled devices should increase for a less diverse and more biased data distribution.

Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV Based Random Access IoT Networks With NOMA

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive $p$-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. To learn an optimal control policy, we first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy among UAVs in which the altitude of UAVs and channel access probability of IoT devices are dynamically and jointly controlled to attain the maximal long-term network capacity while maintaining energy sustainability of UAVs. The proposed algorithm outperforms Deep RL based solutions with reward shaping to account for energy costs, and achieves a temporal average system capacity which is $82.4\%$ higher than that of a feasible DRL based solution, and only $6.47\%$ lower compared to that of the energy-constraint-free system.

A knowledge-based Query Tree with Shortcutting and Couple-Resolution for RFID tag identification

In Radio Frequency Identification (RFID) systems, the reader identifies tags through communications over a shared wireless channel. However, when multiple tags transmit their IDs simultaneously, their signals collide, thereby prolonging the identification delay. However, in some applications, the RFID system has the availability of a database containing the IDs of all the tags that may possibly appear. The present study proposes a novel knowledge-based Query Tree with Shortcutting and Couple Resolution (QTSC) protocol for reducing the identification delay in RFID systems with such a database. In the proposed protocol, a knowledge-based query tree is first constructed to store the queries required to identify all the possible tags in the database. Then, at identifying appearing tags, the tags actually appear among possible tags, shortcutting and couple-resolution techniques are employed to skip redundant queries in the query tree and transmit two ID prefixes simultaneously within the same slot, respectively. The simulation results show that compared to the existing knowledge-based protocols, Knowledge Query Tree (KQT) and Heuristic Query Tree (HQT) protocols, QTSC reduces the identification delay by 60.5% and 39.0%, respectively.

Random Access Communication for Wireless Control Systems With Energy Harvesting Sensors

In this paper, we study wireless networked control systems in which the sensing devices are powered by energy harvesting. We consider a scenario with multiple plants, where the sensors communicate their measurements to their respective controllers over a shared wireless channel. Due to the shared nature of the medium, sensors transmitting simultaneously can lead to packet collisions. In order to deal with this, we propose the use of random access communication policies and, to this end, we translate the control performance requirements to successful packet reception probabilities. The optimal scheduling decision is to transmit with a certain probability, which is adaptive to channel and battery conditions. Moreover, we provide a stochastic dual method to compute the optimal scheduling solution, which is computed across sensors, with only some of the dual variables needed to be shared between nodes. Furthermore, we also consider asynchronicity in the values of the variables across sensor nodes and provide theoretical guarantees on the stability of the control systems under the proposed random access mechanism. Finally, we provide extensive numerical results that corroborate our claims.

Simultaneous Wireless Power and Data Transmission Based on Unsymmetrical Current Waveforms With Duty Cycle Modulation

This paper proposes an active-clamped inverter (ACI) with duty cycle modulation for achieving simultaneous wireless power and data transmission (WPDT). The proposed method adjusts harmonic amplitudes by changing the duty cycle of the inverter, as a means to encode information alongside power using a shared wireless channel. This method generates a variable unsymmetrical current waveform corresponding with different duty cycles. By controlling the duty cycle, simultaneous power and data transfer can be achieved by using the fundamental and harmonic components respectively. The ACI is designed to operate in continuous current mode to reduce the variation of harmonic components, even under no load conditions. A simple ACI containing two switches is proposed to generate unsymmetrical current waveforms with a high efficiency and high circuit quality factor Q. Two communication methods are developed to balance the power transfer capability and signal-noise-ratio (SNR). Various separation distances and loads are tested, with experimental results showing the proposed system can achieve simultaneous power and data transfer across a shared channel in an inductive wireless power transfer.

Reinforcement Learning Based Beamforming Jammer for Unknown Wireless Networks

A jamming attack refers to adversarial activities to cause an interruption of communication among legitimate nodes in a wireless network by transmitting a jamming signal. Among various jamming techniques, network jamming is an attack technique that is performed to maximize the jamming impact on the entire network within the capability of the jammer. In this article, we consider a jamming attack to an unknown wireless network where no a priori network information is provided to the jammer except the radio frequency signal information acquired by overhearing the shared wireless channel. To increase the impact of a jamming attack on an unknown network, we propose a reinforcement learning based beamforming attack strategy. In the proposed attack, a jammer learns the beam direction and angle width to maximize the impact of jamming using the multi-armed bandit technique. As a reward for reinforcement learning, we develop a new metric that can quantitatively evaluate the impact of a jamming attack by measuring the statistical change of the channel busy times before and after each attack. Through extensive simulations, we evaluate the performance of the proposed jamming strategy in an unknown wireless network.

Network Coverage in Interference Limited Wireless Sensor Networks

The performance of a wireless sensor network is limited by the available bandwidth of the shared wireless channel. For the need to share the available bandwidth sensors contend for concurrent transmissions. The simultaneous transmissions by the neighbouring nodes are the main source of interference at the intended receiver sensor. In this paper, we derive the successful decoding probability of receiver sensor in presence of interference and Rayleigh fading channel. We introduce the concept of circular influence zone, to impose a restriction on the aggregated interference from the distant neighbours at an intended receiver sensor. We provide an insight about the critical sensor density which yields an optimal trade-off between the achievable coverage and receiver decoding probability in presence of co-channel interference.

Approximation Algorithms of Minimizing Hidden Pairs in 802.11ah Networks

Recently, 802.11ah network has been widely used to support up to 8,192 nodes to serve various Internet of Things (IoT) communication applications. Because of the large number of nodes, it introduces a group-based MAC protocol, which divides nodes into groups to alleviate the hidden node problem. However, hundreds or even thousands of nodes are assigned into a group to contend the shared wireless channel. As a result, the problem is still serious and it causes a serious decline in system efficiency. Therefore, hidden nodes must be considered when grouping nodes in 802.11ah network. In this paper, the problem of minimizing the number of hidden nodes in group-based 802.11ah networks is expressed as 0/1 integer linear programming (0/1 ILP). In addition, the two additional problems extended from the above problem are also expressed as two 0/1 ILPs. Then, three approximation algorithms are proposed for these three NP-hard problems. The approximation ratio of the obtained feasible solutions to those of the three problems is competitively bounded.

Distributed Optimal Random Access Scheme for Energy Harvesting Devices in Satellite Communication Networks.

This paper considers satellite communication networks where each satellite terminal is equipped with energy harvesting (EH) devices to supply energy continuously, and randomly transmits bursty packets to a geostationary satellite over a shared wireless channel. Packet replicas combined with a successive iteration cancellation scheme can reduce the negative impact of packet collisions but consume more energy. Hence, appropriate energy management policies are required to mitigate the adverse effect of energy outages. Although centralized access schemes can provide better performance on the networks’ throughput, they expend extra signallings to allocate the resources, which leads to non-negligible communication latencies, especially for the satellite communication networks. In order to reduce the communication overhead and delay, a distributed random access (RA) scheme considering the energy constraints is studied. Each EH satellite terminal (EH-ST) decides whether to transmit the packet and how many replicas are transmitted according to its local energy and EH rates to maximize the average long-term network throughput. Owing to the nonconvexity of this problem, we adopted a game theoretic method to approximate the optimal solution. By forcing all the EH-STs to employ the same policy, we characterized and proved the existence and uniqueness of the symmetric Nash equilibrium (NE) of the game. Moreover, an efficient algorithm is proposed to calculate the symmetric NE by combining a policy iteration algorithm and the bisection method. The performance of the proposed RA scheme was investigated via numerous simulations. Simulation results showed that the proposed RA scheme is applicable to the EH devices in the future low-cost interactive satellite communication system.

Dynamics-Aware and Beamforming-Assisted Transmission for Wireless Control Scheduling

The wide application of Internet of Things (IoT) in industrial automation leads to the emergence of a new paradigm of industrial IoT systems, namely wireless control system, where control commands are transmitted from the remote controller to multiple actuators over shared wireless channels. Considering system stability, distinct subsystems usually have different requirements on the transmission quality of control commands due to different system dynamics. In this paper, we aim to simultaneously guarantee the stability of all subsystems and minimize the weighted sum of control cost and transmission cost. To this end, the maximum tolerated packet loss rate of each subsystem is first characterized by a pre-defined Lyapunov function. Then, based on channel conditions and system dynamics, a beamforming-assisted hierarchical coordinated transmission strategy is proposed to alleviate the impact of unreliable transmission on the control performance. The control performance and energy efficiency are further optimized by formulating an overall cost minimization problem constrained by the system stability. Both the differential accumulation and the difference-convex methods are employed to effectively deal with the constraint that is expressed in an implicit probabilistic form. Finally, simulation results demonstrate that the proposed strategy has the advantages of reducing control cost and energy consumption.

Multiuser communication scheme based on binary phase-shift keying and chaos for telemedicine

Background and objectivesCurrently, telemedicine is levered upon the improvement in communication network technology such as Body Area Sensor Networks (BASN) to provided biomedicine solutions. Nevertheless, information security is an important issue since biomedical data is exchanged through insecure channels, which exposes private information that can be intercepted by malicious intruder. Therefore, secure communication protocols for multiuser networks in telemedicine applications are a big challenge. Recent chaos-based encryption works have been conducted in the area of medical secure communications with high security capabilities. However, none of them has considered multiuser network, which is used in several e-health applications. Up to our knowledge, the proposed protocol is the first attempt to consider this service in secure telemedicine. In this paper, we propose a novel scheme based on binary phase-shift key (BPSK) and chaos to provide information security at biosignals in a multiuser network system transmitting data over single channel. MethodsThe proposed scheme uses the two-dimensional Hénon map with enhance pseudorandom sequences and CDMA technique to achieve multiuser encryption process and transmit data over a single channel. We use biosignals such as electrocardiograms (ECG) and blood pressure (PB) signals from PhisioBank ATM data base for simulation results at MatLab software. We evaluate the security and performance by determining the secret key space, secret key sensitivity, resistance against noise attack with quality analysis by using BER, MSE, and PSNR, encryption-decryption time, and throughput. ResultsIn simulations tests, biosignals of ECG and BP in a BANS network are encrypted and transmitted over shared wireless channels and just authorized medical personal can retrieve such information with corresponding secret key from the cryptogram, that appears as noise to any intruder. The proposed multiuser scheme support high noise and interference attacks efficiently in contrast with classic chaos-based encryption works for telemedicine, where some scenarios are simulated with very low BER, very low MSE, and high PSNR between plain biosignals and recovered biosignals when high AWGN noise is added to encrypted-transmitted signal. In addition, the encryption process presents enough key space and high sensitivity at secret key. A comparative analysis of proposed method and recent existing works was also presented. ConclusionsPatients can be monitored and diagnosed opportunely remotely and all their medical information is transmitted securely to the correct specialist. Also, it is possible to transmit several electrophysiological signals in a single channel in a secure multiuser network at low cost optimizing the use of available bandwidth for telemedicine applications.