The advent of 6G is expected to enable many use cases which may rely on accurate knowledge of the location and orientation of user equipment (UE). The conventional localization methods suffer from limitations such as synchronization and high power consumption required for multiple active anchors. This can be mitigated by utilizing a large dimensional passive reconfigurable intelligent surface (RIS). This paper presents a novel low-complexity approach for the estimation of 5D pose (i.e. 3D location and 2D orientation) of a UE in near-field RIS-assisted multiple-input multiple-output (MIMO) systems. The proposed approach exploits the symmetric arrangement of uniform planar array of RIS and uniform linear array of UE to decouple the 5D problem into five 1D sub-problems. Further, we solve these sub-problems using a total least squares ESPRIT inspired approach to obtain closed-form solutions.

This paper considers a communication system where a source sends time-sensitive information to its destination via queues in tandem. We assume that the arrival process as well as the service process (of each server) are memoryless, and each of the servers has no buffer. For this setup, we develop a recursive framework to characterize the mean peak age of information (PAoI) under preemptive and non-preemptive policies with$N$servers having different service rates. For the preemptive case, the proposed framework also allows to obtain mean age of information (AoI).

Reconfigurable intelligent surfaces (RIS) are emerging as a promising technology for 6 G networks due to their ability to shape the radio propagation environment. This ability helps to overcome propagation challenges, such as high path loss, absorption loss, etc., that are particularly needed at millimetre wave (mmWave) bands. Thus, RIS can enhance the capacity of next-generation mmWave communication networks. However, one critical aspect in the design of RIS-aided systems is channel estimation, as it involves estimating two channels (i.e., base stationRIS and RIS-user terminal) separately based on the compound channel observed by the receiver. In this paper, we address this problem by proposing an algorithm that estimates both channels separately by leveraging the advantages of sparsity of the mmWave channel. Specifically, the proposed algorithm uses the parallel factor (PARAFAC) decomposition of the tensor, which is constructed using the received signal matrices observed under different RIS phase shift configurations. Our numerical analysis shows that the proposed algorithm provides significantly smaller normalized mean square error (NMSE) than the widely used alternating least squares (ALS) algorithm, particularly when the number of RIS phase shift configurations is smaller than the number of RIS elements.

Robust spectrum sensing is crucial for facilitating opportunistic spectrum utilization for secondary users (SU) in the absence of primary users (PU). However, propagation environment factors such as multi-path fading, shadowing, and lack of line of sight (LoS) often adversely affect detection performance. To deal with these issues, this paper focuses on utilizing reconfigurable intelligent surfaces (RIS) to improve spectrum sensing in the scenario wherein both the multi-path fading and noise are correlated. In particular, to leverage the spatially correlated fading, we propose to use maximum eigenvalue detection (MED) for spectrum sensing. We first derive exact distributions of test statistics, i.e., the largest eigenvalue of the sample covariance matrix, observed under the null and signal present hypothesis. Next, utilizing these results, we present the exact closed-form expressions for the false alarm and detection probabilities. In addition, we also optimally configure the phase shift matrix of RIS such that the mean of the test statistics is maximized, thus improving the detection performance. Our numerical analysis demonstrates that the MED's receiving operating characteristic (ROC) curve improves with increased RIS elements, SNR, and the utilization of statistically optimal configured RIS.

In our work, we study the age of information (AoI) in a multi-source system where K sources transmit updates of their time-varying processes via a common-aggregator node to a destination node through a channel with packet delivery errors. We analyze AoI for an (α,β,ϵ0, ϵ1)-Gilbert-Elliot (GE) packet erasure channel with a round-robin scheduling policy. We employ maximum distance separable (MDS) scheme at the aggregator for encoding the multi-source updates. We characterize the mean AoI for the MDS coded system. Further, for large blocklengths, we show that the optimal coding rate that achieves maximum coding gain over the uncoded system is $1 - \mathcal{P} - \mathcal{O}(1)$, where $\mathcal{P} \triangleq \frac{\beta }{{\alpha + \beta }}{ \in _0} + \frac{\alpha }{{\alpha + \beta }}{ \in _1}$, and this maximum coding gain is $1 + \mathcal{P} - \mathcal{O}(1)$.

This paper considers a communication system where a source sends time-sensitive information to its destination. We assume that both arrival and service processes of the messages are memoryless and the source has a single server with no buffer. Besides, we consider that the service is interrupted by an independent random process, which we model using an OnOff process. For this setup, we study the age of information for two queueing disciplines: 1) non-preemptive, where the messages arriving while the server is occupied are discarded, and 2) preemptive, where the in-service messages are replaced with newly arriving messages in the Off states. For these disciplines, we derive closed-form expressions for the mean peak age and mean age.

The unmanned aerial vehicle (UAV) based communication is expected to play an important role in enabling a variety of applications in future cellular networks. However, because of the mobility of the UAVs, the communications links involving UAVs undergo large-scale temporal variations in the received signal quality, which may affect the quality-of-service of the underlying application. Therefore, it is crucial to characterize the time-varying process of signal quality observed by the UAVs. In this paper, we consider a scenario in which a cellular-connected UAV acts as a user equipment (UAV-UE), where the locations of base stations (BSs) follow a Poisson point process (PPP) and the UAV-UE is moving along a 3GPP-inspired straight-line trajectory. For this setting, we study the properties of the time-varying successful transmission process that is defined in terms of the time-varying signal-to-noise ratio (SNR) observed at the UAV. In particular, we show that this process is a wide sense stationary (WSS) process and derive its first- and second-order statistics. Finally, we establish an equivalence between the successful transmission processes observed by a UAV-UE served by terrestrial BSs and a terrestrial user served by UAV mounted BSs (UAV-BSs) each moving along an independent straight-line trajectory.

In this paper, we consider a cellular-based Internet of things (IoT) network consisting of IoT devices that can communicate directly with each other in a device-to-device (D2D) fashion as well as send real-time status updates about some underlying physical processes observed by them. We assume that such real-time applications are supported by cellular networks where cellular base stations (BSs) collect status updates over time from a subset of the IoT devices in their vicinity. We characterize two performance metrics: i) the network throughput which quantifies the performance of D2D communications, and ii) the Age of Information which quantifies the performance of the real-time IoT-enabled applications. Concrete analytical results are derived using stochastic geometry by modeling the locations of IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another Independent PPP. Our results provide useful design guidelines on the efficient deployment of future IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

In this paper, we consider a large-scale wireless network consisting of source-destination (SD) pairs where the source nodes frequently send status updates about some underlying physical processes (observed by them) to their corresponding destination nodes. For this setup, we employ age of information (AoI) as a performance metric to quantify freshness of the status updates when they reach the destination nodes. While most of the existing works are focused on the analysis of the temporal mean AoI in deterministic network topologies, we aim to characterize the spatial AoI performance disparity that is inherently present in wireless networks. In particular, we treat the temporal mean AoI as a random variable over space as the update delivery rate of a wireless link is a function of the interference field observed by its receiver. Our objective is to characterize the spatial distribution of the temporal mean AoI observed by the SD pairs by modeling them as a Poisson bipolar process. We first derive accurate bounds on the moments of the successful transmission probability of a status update which are then used to derive tight bounds on the moments as well as the spatial distribution of the temporal mean peak AoI. Our results provide useful design guidelines on the appropriate selection of different system parameters to minimize the mean peak AoI.

This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ age of information (AoI) for quantifying the freshness of the status updates measured at the destination nodes under the preemptive and non-preemptive queueing disciplines with no storage facility. The non-preemptive queue drops the newly arriving updates until the update in service is successfully delivered, whereas the preemptive queue replaces the current update in service with the newly arriving update, if any. As the update delivery rate for a given link is a function of the interference field seen from the receiver, the temporal mean AoI can be treated as a random variable over space. Our goal in this paper is to characterize the spatial distribution of the mean AoI observed by the SD pairs by modeling them as a bipolar Poisson point process (PPP). Towards this objective, we first derive accurate bounds on the moments of success probability while efficiently capturing the interference-induced coupling in the activities of the SD pairs. Using this result, we then derive tight bounds on the moments as well as the spatial distribution of peak AoI (PAoI). Our numerical results verify our analytical findings and demonstrate the impact of various system design parameters on the mean PAoI.

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms theJohnson-Mehl (JM)tessellation, such that the IoT devices located in theJM cellsare allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

This paper provides a comprehensive downlink analysis of non-orthogonal multiple access (NOMA) enabled cellular networks using tools from stochastic geometry. As a part of this analysis, we develop a novel 3GPP-inspired user ranking technique to construct a user cluster for the non-orthogonal transmission by grouping users from the cell center (CC) and cell edge (CE) regions. This technique allows the pairing of users with distinct link qualities, which is imperative for harnessing NOMA performance gains. The analysis is performed from the perspective of the typical cell, which is significantly different from the standard stochastic geometry-based approach of analyzing the performance of the typical user. For this setting, we first derive the moments of the meta distributions for the CC and CE users under NOMA and orthogonal multiple access (OMA). Using this, we then derive the distributions of the transmission rates and mean packet delays under non-real time (NRT) and real-time (RT) service models, respectively, for both CC and CE users. Finally, we study two resource allocation (RA) techniques to maximize the cell sum-rate (CSR) under NRT service, and the sum effective capacity (SEC) under RT service. In addition to providing several useful design insights, our results demonstrate that NOMA provides improved rate region and higher CSR as compared to OMA. In addition, we also show that NOMA provides better SEC as compared to OMA for the higher user density.

This paper presents the meta distribution analysis of the downlink two-user non-orthogonal multiple access (NOMA) in cellular networks. We propose a novel user ranking technique wherein the users from the cell center (CC) and cell edge (CE) regions are paired for the non-orthogonal transmission. Inspired by how users are partitioned in 3GPP cellular models, the CC and CE users are characterized based on the mean powers received from the serving and the dominant interfering BSs. We demonstrate that the proposed technique ranks users in an accurate order with distinct link qualities, which is imperative for the performance of NOMA system. The exact moments of the meta distributions for the CC and CE users under NOMA and orthogonal multiple access (OMA) are derived. In addition, we provide tight beta distribution approximations for the meta distributions and exact expressions of the mean local delays and the cell throughputs for the NOMA and OMA cases. To the best of our knowledge, this is the first comprehensive analysis of NOMA using stochastic geometry with 3GPP-inspired user ranking scheme that depends upon both of the link qualities from the serving and dominant interfering BSs.

The stochastic geometry-based downlink analysis of a cellular network modeled as a Poisson point process (PPP) has traditionally focused on the typical user placed at the origin, which does not lie in the typical cell. In order to characterize the performance of the typical cell, one needs to explicitly consider the point process of users scheduled in a given resource block (RB), which is dependent on the base station (BS) point process. Therefore, we model the locations of the scheduled users using the so-called Type I user process, which places one user uniformly at random in each cell. However, this dependency in the locations of the BSs and users complicates the characterization of the point process of interferers as seen by the typical user of the Type I process. In order to overcome this challenge, we present a general approach to determine the pair correlation function (pcf) of stationary point processes with respect to a reference point. This approach is used to approximate the pcf the point process of interferers with respect to the typical user of the Type I process. With the pcf in hand, we provide the tightest known approximation of the point process of interfering BSs as seen by the typical user of Type I process, which is used to derive remarkably tight expressions for the moments of the downlink signal-to-interference- ratio (SIR) meta distribution for the typical cell.