In recent years, automotive radars with enhanced cognitive abilities have been used on streets for advanced driver assistance systems to improve driving conditions. Consequently, an ego radar encounters increased automotive radar interference, which causes a significant deterioration in its performance. In this work, we propose exploiting the knowledge of street geometry statistics - specifically the street and vehicular density - to optimize the ego radar’s beamwidth. We model the street geometry as a binomial line process with higher street density at the city centers and wider spatial separation between streets in the sub-urban outer areas. The distribution of vehicles on each street is then modeled as a homogeneous one-dimensional Poisson point process. The resulting binomial line Cox process-based stochastic geometry analysis enables a cognitive automotive radar to optimize its beamwidth to maximize detection performance.

We study the impact of a reconfigurable intelligent surface (RIS) in mitigating coverage gaps and enhancing transmission reliability in an industrial internet of things (IIoT) network deployed in a warehouse. First, we study a single blockage scenario and characterize the correlation between blocking events of the base station (BS)-user and the RIS-user links and study its impact on the probability of establishing a viable reflected link. Then, by considering multiple blockages, we derive the distribution of the signal to noise ratio (SNR) as a function of data size, blockage density, and the warehouse size. Finally, we compare the outage performance of this RIS-assisted system with that operated with network-controlled relays, and demonstrate that while the relays provide a higher reliability beyond a certain blockage threshold, increasing the number of RISs may help mitigate this effect. These insights offer valuable design guidelines for deploying RIS-aided IIoT networks in dense blockage environments.

We study the handover (HO) rate and user performance in a large-scale three-dimensional (3-D) unmanned-aerial vehicle (UAV) network, where the UAVs are deployed to provide access to the ground mobile users in a disaster scenario. The 3-D network is modeled as a two-dimensional (2-D) homogeneous marked poisson point process (PPP), in which the marks represent the altitude of the UAVs. Due to the different altitudes marked for the UAVs, irregularly shaped Voronoi cells are formed, which is challenging to analyze. Leveraging the HO rate, we derive the signal-to-interference-plus-noise ratio (SINR) coverage probability and the average throughput experienced by mobile users. Based on the derived framework, we investigate the impact of the intensity of deployment and user speeds on the HO rate and throughput, which enables us to present several key system-design insights for such networks.

The current analysis of wireless networks with transceivers confined to streets is primarily based on Poissonian models, such as Poisson line processes and Poisson line Cox processes. We demonstrate important scenarios where a model with a finite and deterministic number of streets, termed the binomial line process (BLP), is more accurate. We characterize the statistical properties of the BLP and the corresponding binomial line Cox process (BLCP) and apply them to analyze the performance of a network whose access points are deployed along the streets of a city. Such a deployment scenario will be typical for 5G and future wireless networks. In order to obtain a fine-grained insight into the network performance, we derive the meta distribution of the signal-to-interference and noise ratio. Accordingly, we investigate the mean local delay in transmissions and the density of successful transmission. These metrics, respectively, characterize the latency and coverage performance of the network and are key performance indicators of next-generation wireless systems.

We study a novel multi-armed bandit (MAB) setting which mandates the agent to probe all the arms periodically in a non-stationary environment. In particular, we develop TS-GE that balances the regret guarantees of classical Thompson sampling (TS) with the broadcast probing (BP) of all the arms simultaneously in order to detect a change in the reward distributions actively. Once a system-level change is detected, the changed arm is identified by an optional subroutine called group exploration (GE) which scales as$\log_{2}(K)$for a$K$-armed bandit setting. We characterize the probability of missed detection and the probability of false alarms in terms of the environment parameters. The latency of change-detection is upper bounded by$\sqrt{T}$while within a period of$\sqrt{T}$, all the arms are probed at least once. We highlight the conditions in which the regret guarantee of TS-GE outperforms that of the state-of-the-art algorithms, in particular, ADSWITCH and M-UCB. Furthermore, unlike the existing bandit algorithms, TS-GE can be deployed for applications such as timely status updates, critical control, and wireless energy transfer, which are essential features of next-generation wireless communication networks.

The stochastic geometry analysis of vehicular networks and on-street deployment of base stations is largely based on Cox processes driven by Poissonian models. In this paper, we investigate scenarios where a model with a finite and deterministic number of streets, termed the binomial line process (BLP), is more accurate. We characterize the statistical properties of the BLP and the corresponding binomial line Cox process (BLCP). We derive the line length density and the intersection density for the BLP and demonstrate how it models the inhomogeneity of the streets in a city. Finally, leveraging the derived framework, we analyze the performance of a network whose access points are deployed along the streets of a city. Our study captures the variation in the service performance of the users across different locations of a city and thus it leads to key network planning and dimensioning rules for the operators.

Next-generation wireless services are characterized by diverse requirements. To sustain several such applications, the wireless access points need to probe the users in the network periodically in an energy-efficient manner. We study a novel multi-armed bandit (MAB) setting that mandates probing all the arms periodically while keeping track of the best current arm in a piece-wise stationary environment. We developTS-GEthat balances the regret guarantees of the classical Thompson sampling (TS) with the broadcast probing (BP) of all the arms simultaneously in order to actively detect a change in the reward distributions. The main innovation is in identifying the changed arm by an optional indexing subroutine called group exploration (GE) that scales as$\log _{2}(K)$for a$K-$armed bandit setting. We characterize the probability of missed detection and the probability of false alarms in terms of environmental parameters. We highlight the conditions for which the regret guarantee ofTS-GEoutperforms that of the state-of-the-art passively-adaptive and actively-adaptive algorithms, in particular,ADSWITCH. We demonstrate the efficacy ofTS-GEby employing it in two wireless system applications - task offloading in mobile-edge computing (MEC) and an industrial Internet-of-Things (I-IoT) network designed for simultaneous wireless information and power transfer (SWIPT).

Reconfigurable intelligent surfaces (RISs) have recently been envisioned to provide an adaptable channel for futuristic wireless communication networks. In this work, we investigate a downlink RIS-aided non-orthogonal multiple access (NOMA) wireless networks, where a RIS is deployed to enhance users’ performance communicating with the base station (BS) in the presence of a non-ideal transceiver. Specifically, closed-form expressions for the outage, spectral efficiency (SE) and energy efficiency (EE) are derived. The results demonstrate that the non-ideal transceiver has a detrimental impact on the performance of the RIS-aided NOMA system. The proposed analysis also shows that for a RIS-aided NOMA system with a non-ideal transceiver, there exists an upper bound on SE that is independent of the transmit power and the number of reflecting elements in the RIS. Additionally, the performance of the NOMA system is compared with its OMA counterpart, where the results show that for a non-ideal transceiver-based system, the RIS-aided NOMA outperforms OMA.

We study an industry automation scenario where a central controller broadcasts critical messages to the wireless devices (e.g., sensors/actuators). We devise a stochastic geometry framework where the rate coverage probability of devices is modeled by taking into account the density of roaming blockages over the factory floor. To alleviate the loss in the coverage, we adopt a two-phase transmission policy, where in thebroadcast phase, the central controller broadcasts the messages intended for the devices in the network area. The devices in coverage in the broadcast phase act as decode-and-forward relays in therelay phase, so as to reinforce the signal strength at the devices in outage. The total downlink transmission time is, therefore, partitioned into two phases by a tunable factor. Finally, we study the optimal value of the partitioning factor with varying device densities, blockage densities, and file sizes, and we highlight that a longer transmission time should be allotted to the broadcast phase in the case of larger file sizes or lower transmit power of the controller.

Visible light communication (VLC) has recently emerged as an affordable and scalable technology supporting very high data rates for short range vehicle-to-vehicle (V2V) communication. In this work, we advocate the use of vehicular-VLC (V-VLC) for basic safety messages (BSMs) dissemination in lieu of conventional vehicular radio frequency (V-RF) communication in road intersection applications, where the reception performance is affected by interference from the concurrent transmissions of other vehicles. We make use of stochastic geometry to characterize the interference from the same lane as well as the perpendicular lane for various network configurations, i.e., standalone V-VLC, stand-alone V-RF and hybrid V-VLC/V-RF network. Specifically, by modelling the interfering vehicles’ locations as a spatial Poisson point process (PPP), we are able to capture a static two-dimensional road geometry as well as the impact of interference due to vehicles clustering in the vicinity of road intersection in terms of outage probability and throughput. In addition to above, the performance of spatial ALOHA and carrier sense multiple access with collision avoidance medium access control (CSMA/CA MAC) protocol for standalone V-VLC, standalone V-RF and hybrid V-VLC/V-RF network configuration for relaying BSMs at road intersection is also compared. The performance metrics such as delay outage rate (DOR) and information outage rate (IOR) are utilized to investigate the impact of latency associated with various network configurations. Our numerical results reveal that our proposed hybrid V-VLC/V-RF leads to significant improvement in terms of outage performance, throughput and latency as compared to stand-alone V-VLC or stand-alone V-RF network.

Automotive radar is a critical feature in advanced driver-assistance systems. It is important in enhancing vehicle safety by detecting the presence of other vehicles in the vicinity. The performance of radar detection is, however, affected by the interference from radars of other vehicles as well as the variation in the target radar cross-section (RCS) due to varying physical features of the target vehicle. Considering such interference and random RCS, this work provides a fine-grained performance analysis of radar detection. Specifically, using stochastic geometry, we calculate the meta distribution of the signal-to-interference-and-noise ratio that permits the reliability analysis of radar detection at individual vehicles. We also evaluate the delay aspect of radar detection, namely, the mean local delay which is the average number of transmission attempts needed until the first successful target detection. For a given target distance, we obtain the optimal transmit probability that maximizes the density of successful radar detection while keeping the mean local delay below a threshold. We also provide several system design insights in terms of the fraction of reliable radar links, transmission delay, the density of vehicles, and congestion control.

Wireless communications aided by reconfigurable intelligent surfaces (RISs) is a promising way to improve the coverage for cellular users. The controlled reflection of the signal from RISs is especially useful in mm-wave networks when the direct link between a cellular user and its serving base station (BS) is weak or unavailable due to blockages. But the joint blockage of the user-RIS and the user-BS links may significantly degrade the performance of RIS-aided transmissions. This paper aims to study the effect of joint blockages on downlink performance. When the RIS locations are coupled with BS locations, using tools from stochastic geometry, we obtain an optimal placement of RISs either to minimize the joint blockage probability of the user-RIS and the user-BS links or to maximize the downlink coverage probability. The results show that installing RISs on the street intersections improves the coverage probability. For users associated with BSs that are deployed sufficiently close to intersections, the intersection-mounted RISs offer a better coverage performance compared to BS-coupled RISs.

An intelligent reflecting surface (IRS) comprises of an array of discrete reflective elements (REs) possessing re-configurable scattering properties. IRS has the capability to beamform and relays the received radio signal from the transmitter to the desired receiver. This has made the IRS a promising candidate technology for the beyond fifth-generation (5G) cellular standards. In this work, the performance of an IRS-assisted wireless communication system is investigated and compared with the full-duplex (FD) relay-assisted system in the presence of non-ideal transmitters. Specifically, the performance is compared in terms of channel capacity and energy efficiency (EE). The results show that the IRS can never achieve more capacity than the ideal FD relaying, in the presence of a non-ideal transmitter, irrespective of the placement of IRS and relay. Further increasing the number of REs, the capacity saturates with an upper bound that equals to the capacity provided by the relay-assisted system. However, increasing the number of REs results in the reduction of the EE for IRS-assisted systems, which are otherwise known to be highly energy-efficient.

We consider a non-stationary two-armed bandit framework and propose a change-detection-based Thompson sampling (TS) algorithm, named TS with change-detection (TS-CD), to keep track of the dynamic environment. The non-stationarity is modeled using a Poisson arrival process, which changes the mean of the rewards on each arrival. The proposed strategy compares the empirical mean of the recent rewards of an arm with the estimate of the mean of the rewards from its history. It detects a change when the empirical mean deviates from the mean estimate by a value larger than a threshold. Then, we characterize the lower bound on the duration of the time-window for which the bandit framework must remain stationary for TS-CD to successfully detect a change when it occurs. Consequently, our results highlight an upper bound on the parameter for the Poisson arrival process, for which the TS-CD achieves asymptotic regret optimality with high probability. Finally, we validate the efficacy of TS-CD by testing it for edge-control of radio access technique (RAT)-selection in a wireless network. Our results show that TS-CD not only outperforms the classical max-power RAT selection strategy but also other actively adaptive and passively adaptive bandit algorithms that are designed for non-stationary environments.

We study a millimeter wave (mm-wave) wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users. In this network, we propose a mm-wave initial beam-selection scheme based on localization-bounds, which greatly reduces the initial access delay as compared to traditional initial access schemes for standalone mm-wave small cell base station (BS). Then, we introduce a downlink transmission protocol, in which the radio frames are partitioned into three phases, namely, initial access, data, and localization, respectively. We establish a trade-off between the localization and communication performance of mm-wave systems, and show how enhanced localization can actually improve the data-communication performance. Our results suggest that dense BS deployments enable to allocate more resources to the data phase while still maintaining appreciable localization performance. Furthermore, for the case of sparse deployments and large beam dictionary size (i.e., with thinner beams), more resources must be allotted to the localization phase for optimizing the rate coverage. Based on our results, we provide several system design insights and dimensioning rules for the network operators that will deploy the first generation of mm-wave BSs.

In this paper, we consider a cellular network in which the user equipments (UEs) connect to multiple base stations (BSs) for augmenting their signal to interference plus noise ratio (SINR) coverage. In literature, the improvement in the SINR performance with multi-connectivity, or macrodiversity has been well-investigated for several network scenarios, e.g., millimeter wave (mm-wave) networks, networks with random blockages, etc. However, the impact of such multi-connectivity schemes on the network throughput has relatively sparse literature. We bridge this gap by jointly characterizing the effect of multi-connectivity on the SINR coverage and the throughput coverage of the UEs in the network, leveraging tools of stochastic geometry. Such a characterization for generic n connections to the typical user is necessary for 5G and beyond cellular networks, precisely since the degree of multi-connectivity should necessarily be dynamic and self-organizing in nature. Our results show that although the SINR coverage of the UEs always improves with more number of connections, the per-UE throughput in the network may increase or decrease depending on the density and data-rate requirement of the UEs. Interestingly, if the data-rate requirement of the UEs increases, our results show that the UEs should connect to a fewer number of BSs to maximize the per-UE throughput.

In this paper we propose a beam-switching scheme to increase the reliability of the millimeter wave (mm-wave) communication links which are intermittently blocked. We assume that the blockage statistics between the UE equipments (UEs) and the base stations (BSs) change over time. As a result, the data-rates at the UE from different BSs depend not only upon their proximity but also are governed by the blockage dynamics. We model the scenario as a two-armed bandit problem, where playing an arm is analogous to the UE switching the boresight direction of its mm-wave directional beam towards one of the possible BS. In this two-armed bandit problem, we employ a Thompson sampling (TS) algorithm with parameter reset, to enable the UE to select the serving BS. The rewards considered in the algorithm are the spatio-temporal average data-rate coverage probability experienced by the UE, obtained using tools from stochastic geometry. We show that the proposed algorithm outperforms the classical received signal-strength indicator (RSSI) based association scheme in terms of the average rate-coverage probability. The proposed scheme is able to track the changing vehicular blockage dynamics and opportunistically switch between the BS links to maximize the rate-coverage.

International audience

The analyses of cellular network performance based on stochastic geometry generally ignore the traffic dynamics in the network. This restricts the proper evaluation and dimensioning of the network from the perspective of a mobile operator. To address the effect of dynamic traffic, recently, the mean cell approach has been introduced, which approximates the average network load by the zero cell load. However, this is not a realistic characterization of the network load, since a zero cell is statistically larger than a random cell drawn from the population of cells, i.e., a typical cell. In this paper, we analyze the load of a noise-limited network characterized by high signal to noise ratio (SNR). The noise-limited assumption can be applied to a variety of scenarios, e.g., millimeter wave networks with efficient interference management mechanisms. First, we provide an analytical framework to obtain the cumulative density function of the load of the typical cell. Then, we obtain two approximations of the average load of the typical cell. We show that our study provides a more realistic characterization of the average load of the network as compared to the mean cell approach. Moreover, the prescribed closed-form approximation is more tractable than the mean cell approach.

We analyze a millimeter wave network, deployed along the streets of a city, in terms of positioning and downlink data-rate performance, respectively. First, we present a transmission scheme where the base stations provide jointly positioning and data-communication functionalities. Accordingly, we study the trade- off between the localization and the data rate performance based on theoretical bounds. Then, we obtain an upper bound on the probability of beam misalignment based on the derived localization error bound. Finally, we prescribe the network operator a scheme to select the beamwidth and the power splitting factor between the localization and communication functions to address different quality of service requirements, while limiting cellular outage.

We characterize a two tier heterogeneous network, consisting of classical sub-6 GHz macro cells, and multi radio access technology (RAT) small cells able to operate in sub-6 GHz and millimeter-wave (mm-wave) bands. For optimizing coverage and to balance loads, we propose a two-step mechanism based on two biases for tuning the tier and RAT selection, where the sub-6 GHz band is used to speed-up the initial access procedure in the mm-wave RAT. First, we investigate the effect of the biases in terms of signal-to-interference-plus-noise ratio (SINR) distribution, cell load, and user throughput. More specifically, we obtain the optimal biases that maximize either the SINR coverage or the user downlink throughput. Then, we characterize the cell load using the mean cell approach and derive upper bounds on the overloading probabilities. Finally, for a given traffic density, we provide the small cell density required to satisfy system constraints in terms of overloading and outage probabilities. Our analysis highlights the importance of deploying dual-band small cells, in particular, when small cells are sparsely deployed or in case of heavy traffic.

We characterize a multi tier network with classical macro cells, and multi radio access technology (RAT) small cells, which are able to operate in microwave and millimeter-wave (mm-wave) bands. The small cells are assumed to be deployed along roads modeled as a Poisson line process. This characterization is more realistic as compared to the classical Poisson point processes typically used in literature. In this context, we derive the association and RAT selection probabilities of the typical user under various system parameters such as the small cell deployment density and mm-wave antenna gain, and with varying street densities. Finally, we calculate the signal to interference plus noise ratio (SINR) coverage probability for the typical user considering a tractable dominant interference based model for mm-wave interference. Our analysis reveals the need of deploying more small cells per street in cities with more streets to maintain coverage, and highlights that mm-wave RAT in small cells can help to improve the SINR performance of the users.

The future demands of high data spectral efficiency and ubiquitous coverage are pushing the next generation of cellular networks towards network densification with massive deployments of small cells to complement macro base stations. In this paper, we study a network comprising of closed access small cells along with macro base stations using stochastic geometry. First, the cell association probability is characterized. Additionally, approximate values of the average downlink signal to interference and noise ratio (SINR) and the downlink spectral efficiency are derived. These derivations are carried out without using the classical approach of solving a Laplace functional. For this, the statistical independence of the useful signals and interference powers is exploited. The obtained results can be used to optimize the small cell network deployment and the inter-cell interference coordination functions.