Channel state information (CSI) as essential knowledge in communications has recently been exploited by WiFi and Internet-of-Things (IoT) devices to achieve sensing such as human activity and event detection. This passive sensing paradigm in WiFi/IoT systems poses a new privacy threat since CSI is easily accessible by user/IoT devices (UDs), but it is hard to know whether a UD analyzes CSI to extract environmental information and to prevent it from doing so. This paper presents a theoretical study of how a WiFi/IoT access point (AP) can privately precode signals, coined anti-sensing precoding, to impact a UD's CSIbased sensing upon AP-UD communication. To draw insights into environmental information privacy, we model and derive UD's sensing performance metrics in closed form. Two possible cases in which a UD has prior or online CSI knowledge as a basis for sensing decisions are studied. The optimal anti-sensing precoding that minimizes UD's correct decision probability in each case is derived and analyzed. Further, the influences of the precoding on UD's communication performance are discussed. In simulations, we validate our theoretical results and reveal the benefit and cost of the precoding to anti CSI-based passive sensing.

Passive sensing has garnered substantial attention in e-healthcare and smart home applications for the appealing concept of detecting human occupancy and activities based on channel state information (CSI) in wireless signal transmissions. Despite the promising benefits of passive sensing, what is less mentioned is the privacy issue raised. It is difficult to prevent a communication device from analyzing CSI to learn private environmental information. This paper investigates the use of a reconfigurable intelligent surface (RIS) by a system planner (SP) to assist a user device (UD) Communication and meanwhile Anti its Passive Sensing based on CSI, named RIS-CAPS. We address the fundamental UD passive sensing performance in identifying changes in the environment, called environmental states, by CSI estimation and reveal the tradeoff between communication and system privacy when a RIS is controlled to influence wireless channels. We report a shortboard effect that to anti UD’s sensing, the UD’s communication performance in different environmental states will be determined by the state having the worst channel condition. Furthermore, we analyze and tackle the new RIS configuration optimization problem to improve UD’s communication while constraining its passive sensing performance. A solution algorithm that exploits alternating optimization and semidefinite relaxation techniques is developed to address the challenging RIS configuration optimization. Finally, extensive simulations validate the performance modeling and analysis in our study. Insights into when RIS is more capable to anti passive sensing are provided.

Passive sensing that extracts human occupancy and activity information from wireless signal propagation properties has drawn growing interest in recent years. With wireless signals pervasively accessible, passive sensing brings both opportunities and threats to privacy. To leverage the sensing benefits, improving passive sensing accuracy and granularity is pursued. However, in turn, this increases the difficulty of protecting information. In this study, we investigate the use of reconfigurable intelligent surface (RIS) to influence passive sensing for different needs, i.e., to support or to prevent sensing. This paper presents the first theoretical performance analysis of RIS-enabled passive sensing and thereof, addresses the optimal RIS configurations that can improve or degrade the sensing accuracy as desired. In specific, with knowledge of channel features in target sensing scenes of interest, we model the passive sensing performance by a signal receiver to identify the target scenes when a RIS is utilized in the environment. We derive closed-form probability metrics of correct sensing, i.e., the accuracy, and false sensing of the scenes, with which we optimize RIS configuration to maximize/minimize the sensing accuracy. A heuristic algorithm, termed RIS- PS, is developed, solving the RIS configurations in a low complexity. In simulations, we verify our theoretical results and the performance of RIS-PS. Results show RIS's ability in both light-of-sight and non-light-of-sight passive sensing scenarios.

Reinforcement learning (RL) has recently emerged as a promising solution for intelligence bandwidth decisions that reduce latency in optical access networks. Even though RL drives model-free self-adaptive bandwidth decisions, the learning time cost and the widely-known exploration-exploitation dilemma of when to apply the best decision learnt are challenging to address in the bandwidth decision context. This paper for the first time exploreshow to rapidly learn an optimal bandwidth decision with a known confidence level of the decisionfor minimizing optical access network latency. We investigate critical aspects, including reward acquisition and strategies to explore decisions, in an RL-based bandwidth allocation scheme. Applying renewal theory, we address the timing for the central office (CO) to acquire rewards from optical network units for accurate decision value evaluation. Further, we derive the relationship between the decision practice times and the confidence of the optimal decision in closed-form. A reward variance-oriented (RVO) exploration strategy is proposed, in which the CO selects bandwidth decisions with probabilities proportional to the reward variances. We prove that the RVO is the most time efficient in learning an optimal decision with a confidence guarantee. With numerical and extensive simulations, we validate the theory and compare several common strategies with the RVO.

To account for the ever-increasing demand for wireless broadband, it is critical to develop new techniques that can increase throughput. Network-coded (NCed) hybrid automatic repeat request (HARQ) has previously been shown to hold much potential for increasing network throughput by utilizing the retransmissions and side-information. However, the previous performance analysis did not take into account that the implementation of NCed HARQ requires additional control information that indicates which packets are included in one network coded packet and additional ARQ acknowledgements for the packets overheard by the unintended users. In this paper, we analyze the resources required for additional overhead and derive the outage probability and throughput for both downlink and uplink. With the analytical expressions and Monte Carlo simulation results, we study the trade-off between the maximum number of users (MNU) for NCed HARQ system and the overall throughput. Finally, we numerically show the existence of the optimal MNU and derive an approximate closed-form expression for the optimal value.

Network-coded (NCed) automatic repeat request (ARQ) techniques have been shown to provide significant throughput improvements over basic ARQ systems in two-way wireless systems. Most results derived so far, however, used the assumption of no extra overhead. In practical systems, NCed-ARQ requires more information exchange between base-station and end-nodes, and therefore it is crucial to study the impact of the extra-overhead on such systems. In this paper, we analyze the performance of a practical NCed-ARQ system. We assume M end-users wish to exchange information with a base-station. We derive first the average number of extra acknowledgments required to facilitate NCed-ARQ scheme. Then, we derive both downlink and uplink throughput expressions and study the tradeoff between feedback and re-transmission. Finally, we numerically optimize the throughput with respect to the number of end-users.

In this paper, we consider a multiple-access broadcast channel (MABC) with ARQ feedback, in which M endusers wish to exchange messages with a central node or basestation. In this scenario, an end-user may overhear other endusers' messages prior to the re-transmission phase. We propose a new network coded (NCed)-ARQ scheme with reverse-link-assistance (RLA) that exploits this overheard information in uplink transmission to increase the downlink throughput. We derive throughput expressions for the new NCed-ARQ scheme in wireless additive white Gaussian noise with Rayleigh fading channel, which we numerically evaluate. For low/moderate SNRs, NCed-ARQ with RLA greatly improves the performance of downlink throughput.