Having timely and fresh knowledge about the current status of information sources is critical in a variety of applications, where the status update arrives at the destination later than its generation time due to processing and communication delays. The freshness of the status update at the destination is captured by the notion of the age of information. In this study, we analyze a multiple sensing network with multiple sources, multiple servers, and a monitor (destination). Each source corresponds to an independent piece of information, and its age is individually measured. Given a particular source, the servers independently sense the source of information and send the status update to the monitor. We assume that updates arrive at the servers according to Poisson random processes. Each server sends its updates to the monitor through a direct link, which is modeled as a queue. The service time to transmit an update is considered to be an exponential random variable. We examine both homogeneous and heterogeneous service and arrival rates for the single-source case, and homogeneous arrival and service rates for the multiple-source case. We derive a closed-form expression for the average age of information under a last-come-first-serve (LCFS) queue for a single source and an arbitrary number of homogeneous servers. Using a recursive method, we derive the explicit average age of information for any number of sources and homogeneous servers. We also investigate heterogeneous servers and a single source, and present efficient algorithms for finding the average age of information. Optimal update scheduling strategies are also investigated in several scenarios, providing insights into enhancing the system performance in terms of update freshness.

Intelligent integrated sensing and communication is one of key aspects of future wireless networks in which sensing can be leveraged to enhance communications and vice-versa. In this paper, we propose a novel sensing solution that can be used to represent an RF-environment. The proposed solution accounts for practical challenges such as limited time resolution due to limited bandwidth with no angle measurements while providing robustness to wireless propagation phenomena such as diffraction. Our proposed method leverages offline data collection during RF-mapping, and finds the location of virtual anchors (VAs), i.e., mirror images of a physical anchor w.r.t reflectors, through an iterative process called successive tap removal (STR). Afterwards, machine learning (ML) models are trained to predict dominant multipath components of the received wireless channel at a given location. Found VAs and their associated ML models stand for intermediate entities that represent an RF-environment. As an application, we use the developed models in the context of multipath assisted positioning to improve positioning accuracy in challenging indoor environments with heavy non-line-of-sight (NLoS) conditions. Finally, we extend our ideas to systems with multi-antenna transmitters and show that VA detection accuracy can be improved, bringing higher accuracy to the downstream positioning applications.

We consider a status updating system where having timely knowledge about the information source at the destination (monitor) is of utmost importance. By utilizing the age of information (AoI) metric, the freshness of the status update over an erasure channel is investigated. Due to the erasure nature of the update transmission, an error-free feedback channel from the monitor to the source is beneficial for reducing AoI. Each status update contains K packets which can be sent through the channel one at a time. At each channel use, one status update is available from the information source. Depending on how many packets have been received successfully out of the K packets, we need to decide whether to continue sending the current update or terminate it and start sending the newly generated update. In this paper, we find the optimal failure tolerance when the erasure probability (ϵ) is in the regime ϵ→ 0 and also provide a lower and an upper bound for the average AoI for all erasure probabilities. Moreover, for all ϵ, we provide a lower bound for failure tolerance to minimize peak AoI.

Having timely and fresh knowledge about the current state of information sources is critical in a variety of applications. In particular, a status update may arrive at the destination much later than its generation time due to processing and communication delays. The freshness of the status update at the destination is captured by the notion of age of information. In this study, we first analyze a network with a single source, n servers, and the monitor (destination). The servers independently sense the source of information and send the status update to the monitor. We then extend our result to multiple independent sources of information in the presence of n servers. We assume that updates arrive at the servers according to Poisson random processes. Each server sends its update to the monitor through a direct link, which is modeled as a queue. The service time to transmit an update is considered to be an exponential random variable. We examine both homogeneous and heterogeneous service and arrival rates for the single-source case, and only homogeneous arrival and service rates for the multiple-source case. We derive a closed-form expression for the average age of information under a last-come-first-serve (LCFS) queue for a single source and arbitrary n homogeneous servers. For n = 2, 3, we derive the explicit average age of information for arbitrary sources and homogeneous servers, and for a single source and heterogeneous servers. For n = 2, we find the optimal arrival rates given fixed sum arrival rate and service rates.

Resistive memories are considered a promising memory technology enabling high storage densities. However, the readout reliability of resistive memories is impaired due to the inevitable existence of wire resistance, resulting in the sneak path problem. Motivated by this problem, we study polar coding over channels with different reliability levels, termed non-stationary polar codes, and we propose a technique improving the bit error rate (BER) performance. We then apply the framework of non-stationary polar codes to the crossbar array and evaluate its BER performance under two modeling approaches, namely binary symmetric channels and binary asymmetric channels. Finally, we propose a technique for biasing the proportion of high-resistance states in the crossbar array and show its advantage in reducing further the BER. Several simulations are carried out using a SPICE-like simulator, exhibiting significant reduction in BER.

In a distributed storage system, recovering from multiple failures is a critical and frequent task that is crucial for maintaining the system's reliability and fault-tolerance. In this paper, we focus on the problem of repairing multiple failures in a centralized way, which can be desirable in many data storage configurations; furthermore, we show that a significant repair traffic reduction is possible. First, the fundamental trade-off between the repair bandwidth and the storage size for functional repair is established. Using a graph-theoretic formulation, the optimal tradeoff is identified as the solution to an integer optimization problem, for which a closed-form expression is derived. Expressions of the extreme points, namely the minimum storage multi-node repair (MSMR) and minimum bandwidth multinode repair (MBMR) points, are obtained. Second, we describe a general framework for converting single erasure minimum storage regenerating codes to MSMR codes. The repair strategy for e failures is similar to that for a single failure; however, certain extra requirements need to be satisfied by the repairing functions for a single failure. For illustration, the framework is applied to product-matrix codes and interference alignment codes. Furthermore, we prove that the functional MBMR point is not achievable for linear exact-repair codes. We also show that the exact-repair minimum bandwidth cooperative repair codes achieve an interior point, that lies near the MBMR point, when k ≡ 1 mod e, k being the minimum number of nodes needed to reconstruct the entire data. Finally, for k > 2e, e | k, and e | d, where d is the number of helper nodes during repair, we show that the functional repair trade-off is not achievable under exact repair, except for maybe a small portion near the MSMR point, which parallels the results for single-erasure repair by Shah et al.

We study the problem of centralized exact repair of multiple failures in distributed storage. We describe constructions that achieve a new set of interior points under exact repair. The constructions build upon the layered code construction by Tian et al in [1], designed for exact repair of single failure. We firstly improve upon the layered construction for general system parameters. Then, we extend the improved construction to support adaptive repair for a flexible number of failures, and a flexible number of helpers. In particular, we prove the optimality of one point on the functional repair tradeoff of multiple failures for some parameters. Finally, considering minimum bandwidth cooperative repair (MBCR) codes as centralized repair codes, we determine explicitly the best achievable region obtained by space-sharing among all known points, including the MBCR point.

In distributed storage, erasure codes are widely used to provide data reliability, where every codeword symbol corresponds to one storage node. The network traffic cost during the repair of node failures, called repair bandwidth, is an important metric in code design. In particular, minimum storage regenerating (MSR) codes are maximum distance separable (MDS) codes that have optimal repair bandwidth. In this paper, we generalize the problem to minimum storage multi-node regenerating (MSMR) codes, which are MDS codes with optimal repair bandwidth for e node failures. We describe a general framework for converting MSR codes to MSMR codes. The repair strategy for e failures is similar to that for single failure, however certain extra requirements need to be satisfied by the repairing functions for single failure. Then we apply this framework to product-matrix codes and interference alignment codes.

We study the problem of secret-key agreement between two legitimate parties, Alice and Bob, in the presence of an eavesdropper Eve. There is a public channel with unlimited capacity that is available to the legitimate parties and is also observed by Eve. Our focus is on Rayleigh fading quasistatic channels. The legitimate receiver and the eavesdropper are assumed to have perfect channel knowledge of their channels. We study the system in the high-power regime. First, we define the secret-key diversity gain and the secret-key multiplexing gain. Second, we establish the secret-key diversity multiplexing tradeoff (DMT) under no channel state information (CSI) at the transmitter (CSI-T). The eavesdropper is shown to “steal” only transmit antennas. We show that, like the DMT without secrecy constraint, the secret-key DMT is the same either with or without full channel state information at the transmitter. This insensitivity of secret-key DMT toward CSI-T features a fundamental difference between secret-key agreement and the wiretap channel, in which secret DMT depends heavily on CSI-T. Finally, we present several secret-key DMT-achieving schemes in case of full CSI-T. We argue that secret DMT-achieving schemes are also key DMT-achieving. Moreover, we show formally that artificial noise (AN), likewise zero-forcing (ZF), is DMT-achieving. We also show that the public feedback channel improves the outage performance without having any effect on the DMT.

We consider secret-key agreement with public discussion over multiple-input multiple-output (MIMO) Rayleigh fast-fading channels under correlated environment. We assume that transmit, legitimate receiver and eavesdropper antennas are correlated. The legitimate receiver and the eavesdropper are assumed to have perfect channel knowledge while the transmitter has only knowledge of the correlation matrices. First, we derive the expression of the secret-key capacity under the considered setup. Then, we prove that the optimal transmit strategy achieving the secret-key capacity consists in transmitting independent Gaussian signals along the eingenvectors of the transmit correlation matrix. The powers allocated to each channel mode are determined as the solution to a numerical optimization problem that we derive. A necessary and sufficient condition for beamforming (i.e., transmitting along the strongest channel mode) to be capacity-achieving is derived. Finally, we analyze the impact of correlation matrices on the system performance and provide closed-form expressions of the gain/loss due to correlation in the high power regime.

We consider a two-user interference channel assisted by a relay. Treating interference as noise at the receivers, and adopting an amplify and forward (AF) strategy at the relay, we derive achievable rates of both users, for given powers. Next, we solve the optimal power allocation problem maximizing the weighted sum rate of both users with and without relay power optimization. In particular, we propose a simple iterative line search algorithm solving the joint optimization problem over the three transmit powers and show that optimizing the relay power enhances the performance of the system. Then, considering the first user as a primary user, we determine the maximum instantaneous rate that the secondary user can achieve subject to an outage constraint with respect to the primary user and a peak power constraint. We show that, likewise the first part, jointly optimizing the secondary user and the relay transmit powers enhances the secondary user performance.