Intelligent Reflecting Surfaces (IRS) are a promising solution to enhance the coverage of future wireless networks by tuning low-cost passive reflecting elements (referred to as metasurfaces ), thereby constructing a favorable wireless propagation environment. Different from prior works, which assume Rayleigh fading channels and do not consider the direct link between a base station and a user, this article develops a framework based on moment generation functions (MGF) to characterize the coverage probability of a user in an IRS-aided wireless systems with generic Nakagami-m fading channels in the presence of direct links. In addition, we demonstrate that the proposed framework is tractable for both finite and asymptotically large values of the metasurfaces. Furthermore, we derive the channel hardening factor as a function of the shape factor of Nakagami-m fading channel and the number of IRS elements. Finally, we derive a closed-form expression to calculate the maximum coverage range of the IRS for given network parameters. Numerical results obtained from Monte-Carlo simulations validate the derived analytical results.

The unrelenting increase in the population of mobile users and their traffic demands drive cellular network operators to densify their network infrastructure. Network densification shrinks the footprint of base stations (BSs) and reduces the number of users associated with each BS, leading to an improved spatial frequency reuse and spectral efficiency, and thus, higher network capacity. However, the densification gain comes at the expense of higher handover rates and network control overhead. Hence, user’s mobility can diminish or even nullifies the foreseen densification gain. In this context, splitting the control plane ( $C$ -plane) and user plane ( $U$ -plane) is proposed as a potential solution to harvest densification gain with reduced cost in terms of handover rate and network control overhead. In this paper, we use stochastic geometry to develop a tractable mobility-aware model for a two-tier downlink cellular network with ultra-dense small cells and $C$ -plane/ $U$ -plane split architecture. The developed model is then used to quantify the effect of mobility on the foreseen densification gain with and without $C$ -plane/ $U$ -plane split. To this end, we shed light on the handover problem in dense cellular environments, show scenarios where the network fails to support certain mobility profiles, and obtain network design insights.

Using stochastic geometry tools, we develop a systematic framework to characterize the meta distributions of the downlink SIR/SNR and data rate of the typical device in a cellular network with coexisting sub-6GHz and millimeter wave (mm-wave) spectrums. Macro base-stations (MBSs) transmit on sub-6GHz channels (which we term “microwave” channels), whereas small base-stations (SBSs) communicate with devices on mm-wave channels. The SBSs are connected to MBSs via a microwave (μwave) wireless backhaul. The μwave channels are interference limited and mm-wave channels are noise limited; therefore, we have the meta distribution of SIR and SNR in μwave and mm-wave channels, respectively. To model the line-of-sight (LOS) nature of mm-wave channels, we use Nakagami-m fading model. To derive the meta distribution of SIR/SNR, we characterize the conditional success probability (CSP) (or equivalently reliability) and its bth moment for the typical device (a) when it associates to a μwave MBS for direct transmission, and (b) when it associates to a mm-wave SBS for dual-hop transmission (backhaul and access transmission). Performance metrics such as the mean and variance of the local delay (network jitter), mean of the CSP (coverage probability), and variance of the CSP are derived. Closed-form expressions are presented for special scenarios. The extensions of the developed framework to the μwave-only network or mm-wave only networks where SBSs have mm-wave backhauls are discussed. Numerical results validate the analytical results. Insights are extracted related to the reliability, coverage probability, and latency of the considered network.

We study the fundamental problem of spectrum allocation and device association in uplink two-hop Internet of Things (IoT) networks under two spectrum allocation schemes: 1) orthogonal spectrum partition (OSP) and 2) full spectrum reuse (FSR). We propose a novel analytical model to estimate the uplink data rate utility function, which takes into account power control fractional and spatial density of aggregators. We then compute the optimal aggregator association bias (for the FSR scheme) and the optimal joint spectrum partition ratio and optimal aggregator association bias (for the OSP scheme) using constraint gradient ascent optimization. Using the above obtained optimal values and the proposed model, we compare the performance of the optimized OSP and FSR schemes with the benchmark maximum-SIR-based association scheme and the minimum-distance association scheme in terms of the cumulative distribution function of device uplink data rate. By optimizing key network parameters, namely the spectrum partition ratio and aggregator association bias, we mitigate interference and enhance the mean uplink per-device data rate for both FSR and OSP. To the best of our knowledge, this paper is the first that proposes an analytical model to estimate the log utility of the uplink data rate of two-hop IoT networks.