This paper investigates the impact of SNR (Signal-to-Noise Ratio)-based threshold scheduling on IoT (Internet of Things) uplink network performance. Threshold scheduling is a low-complexity technique utilizing channel state information (CSI) to boost network performance by only allowing transmission when the received SNR at the receiver is greater than a certain threshold. We use stochastic geometry to derive analytical and asymptotic expressions of the transmission success probability, active probability, and spatial capacity and characterize the network performance. We obtain a novel asymptotic bound for the signal-to-interference-plus-noise ratio (SINR) distribution of threshold scheduling. Furthermore, we adopt physical layer security to enhance the network security of threshold scheduling utilizing artificial noise. Based on these results, we optimize the spatial capacity of threshold scheduling under the constraints of reliability, security, and latency, then introduce a computationally efficient algorithm that finds the optimal SNR threshold maximizing the spatial capacity for a resource-limited IoT network. Various numerical results are provided to furnish the findings and gain insights to optimize the design of threshold scheduling.

Intelligent Reflecting Surface (IRS) is a communication technology that can control the phase shift and reflection of the incoming signal towards the destination, achieving high spectral efficiency at a low hardware cost. However, the IRS-assisted wireless networks pose fundamental challenges on statistical channel modeling. Communication assisted by the IRS takes the form of a mixture channel, composed of a direct link and cascaded link aided by the IRS, which is often intractable to analyze, requires advanced functions, such as Meijer’s G or Fox’s H functions, to describe, and only applies to a certain operating frequency or network environment. These limitations motivate the development of a tractable and highly accurate channel model for IRS-assisted wireless networks, but versatile enough to be applied to any frequency band and communication scenario given proper parameterization. To this end, we utilize the mixture Gamma distributions to model IRS-assisted communication and derive distributions of the mixture channel for both multiplicability and quadratic form. The system performance of the IRS-assisted wireless network is analyzed using stochastic geometry, and the approximation accuracy of the proposed channel model is validated through extensive numerical simulation. These results indicate that the mixture Gamma distribution-based approximation can greatly facilitate the modeling and analysis in IRS-assisted networks with high accuracy.

The hybrid intelligent reflecting surface (IRS) architecture is a novel technology that leverages the advantages of both passive and active IRS; the passive IRS offers a large aperture, while the active IRS provides additional power amplification. Prior studies have shown that the optimal performance of IRS-assisted wireless networks is achieved when the passive IRS is deployed near the transceivers and the active IRS is near the receiver, assuming transceivers with limited height. However, most of the prior works on hybrid IRS blindly adopted this assumption in the IRS association policy, which essentially becomes a partial selection strategy that offers analytical simplicity at the cost of sub-optimal performance. This limitation motivated us to find the globally optimal deployment strategy for all types of IRS. To this end, we first employ thegeometric modelsfor integrated path loss distance (known as Cassini oval and Ellipse for product- and sum-distance path loss laws, respectively) and use them to determine the optimal locations of the hybrid IRS. Then, we design a novelopportunistic association policyfor hybrid IRS based on the integrated path loss model. Furthermore, we validate our proposed methods through simulations and show that they significantly outperform the conventional nearest association policy, especially for hybrid and active IRS.

In this contribution we evaluate the transmission of confidential information over ${\mathcal{F}}$ composite fading channels in the presence of an eavesdropper (Eve) who also experiences ${\mathcal{F}}$ composite fading. Upon obtaining tractable closed-form expressions for the secure outage probability and the probability of strictly positive secrecy capacity, we analyze extensively the achievable physical layer security performance in the context of mm-wave communications. This is realized with the aid of extensive measurement results from realistic communication scenarios, which show the behavior of composite ${\mathcal{F}}$ fading channels in device-to-device communication scenarios. The offered results provide meaningful insights of theoretical and practical importance that are expected to be useful in the design of mm-wave based communication systems.

Positioning based on received signal strength (RSS) is regarded as a promising candidate for localization purposes in wireless networks due to its feasibility and deployability. In general, multilateration and fingerprinting algorithms are the primary localization methods in RSS-based localization systems, which are assessed by the Cramér–Rao lower bound (CRLB), given fixed node locations, including the target and participating anchors. However, this methodology produces only definite values for the CRLB specific to the scenario of interest while does not provide insights into the fundamental limits of localization performance. Thus, we are motivated to analyze the RSS-based localization performance using stochastic geometry to allow for randomly distributed nodes and investigate how the nodes’ locations influence this performance. To characterize the localization performance of the multilateration method, a tractable expression of localizability is provided to indicate the probability that a target is localizable. Then, conditioned on the number of participating anchors$L$, we provide an accurate approximation of the CRLB using the$\lceil L/4 \rceil $th value of ordered distances to quantify the localization accuracy on a random network setting and examine how its performance is influenced under different propagation channels by utilizing$\kappa $–$\mu $shadowed fading. Next, the fingerprinting localization problem is regarded as a hypothesis testing problem, and thus, its performance can be evaluated based on the similarity analysis of the observed RSS fingerprints. A comprehensive analysis of these two methods is performed, and the derived calculations are compared with the experimental results to demonstrate that our unified framework can precisely reflect localization performance in real-world scenarios. Based on the analysis, we can develop an insight to optimally design an RSS-based localization system that achieves the specified localization requirements.

Unbounded growth in cellular traffic is continuing to increase network power consumption meaning that the need for energy efficient cellular network design is more critical than ever. To find the trade-off between the spectral and energy efficiency, stochastic geometry has been widely employed where the cellular nodes are considered as being distributed according to a Poisson point process (PPP). Most of the prior works using stochastic geometry commonly assumed Rayleigh fading as the de facto fading model due to its tractability and simplicity. However, the propagation environments in which emerging cellular networks will operate, are diverse in nature, constituted by many different propagation phenomena which can not be fully captured by the Rayleigh distribution. To incorporate these physical attributes into the calculation of the network performance metrics, we consider κ-μ shadowed fading, which contains the majority of the well-known fading models as special cases. Using stochastic geometry, we evaluate the spectral efficiency and energy efficiency of a K-tier HetNet with K classes of BSs, differing in terms of the transmit power, BS density, shadowing and fading. Through numerical evaluation, we observe a trade-off relationship between the shadowing and fading parameters, spectral efficiency and energy efficiency, which provides important new insights into energy efficient network design.

In this paper, we perform a systematic investigation of the statistics associated with the product of two independent and non-identically distributed κ-μ random variables. More specifically, we develop novel analytical formulations for many of the fundamental statistics of interest, namely, the probability density function, cumulative distribution function, and moment-generating function. Using these new results, closedform expressions are obtained for the higher order moments, amount of fading and channel quality estimation index, while analytical formulations are obtained for the outage probability, average channel capacity, average symbol error probability, and average bit error probability. These general expressions can be reduced to a number of fading scenarios, such as the double Rayleigh, double Rice, double Nakagami-m, κ-μ/Nakagami-m, and Rice/Nakagami-m, which all occur as special cases. Additionally, as a byproduct of the work performed here, formulations for the κ-μ/κ-μ composite fading model can also be deduced. To illustrate the efficacy of the novel expressions proposed here, we provide useful insights into the outage probability of a dualhop system used in body area networks, and demonstrate the suitability of the κ-μ/κ-μ composite fading for characterizing shadowed fading in device-to-device channels.

In this paper, we initially consider the product of two independent and non-identically distributed κ-μ variates, and obtain its probability density function (PDF) in novel analytical form. Following from this, the PDF for the κ-μ/κ-μ composite fading model is then deduced. Monte-Carlo simulations are performed to verify the derived results. It is worth highlighting that both sets of expressions can be reduced to a number of double and composite fading scenarios such as the double Rice, double Nakagami-m, κ-μ/Nakagami-m, and Rice/Nakagami-m. Finally, we illustrate the usefulness of the obtained formulations by characterizing the shadowed fading encountered in body area networks, device-to-device and vehicle-to-vehicle communication channels. To this end, it is shown that the κ-μ/κ-μ composite fading model provides an excellent fit to the measurement data.

Device-to-device (D2D) communications are recognized as a key enabler of future cellular networks, which will help to drive improvements in spectral efficiency and assist with the offload of network traffic. Relay-assisted D2D communications will be essential when there is an extended distance between the source and the destination or when the transmit power is constrained below a certain level. Although a number of works on relay-assisted D2D communications have been presented in the literature, most of those assume that relay nodes cooperate unequivocally. In reality, this cannot be assumed, since there is little incentive to cooperate without a guarantee of future reciprocal behavior. To incorporate the social behavior of D2D nodes, we consider the decision to relay using the donation game based on social comparison, characterize the probability of cooperation in an evolutionary context and then evaluate the network performance of relay-assisted D2D communications. Through numerical evaluations, we investigate the performance gap between the ideal case of 100% cooperation and practical scenarios with a lower cooperation probability. It shows that practical scenarios achieve lower transmission capacity and higher outage probability than idealistic network views, which assume full cooperation. After a sufficient number of generations, however, the cooperation probability follows the natural rules of evolution and the transmission performance of practical scenarios approach that of the full cooperation case, indicating that all D2D relay nodes adapt the same dominant cooperative strategy based on social comparison, without the need for external enforcement.

Device-to-device (D2D) communications are now considered an integral part of future 5G networks, which will enable direct communication between user equipments and achieve higher throughputs than conventional cellular networks, but with the increased potential for co-channel interference. The physical channels, which constitute D2D communications, can be expected to be complex in nature, experiencing both line-ofsight (LOS) and non-LOS conditions across closely located D2D pairs. In addition to this, given the diverse range of operating environments, they may also be subject to clustering of the scattered multipath contribution, i.e., propagation characteristics which are quite dissimilar to conventional Rayleigh fading environments. To address these challenges, we consider two recently proposed generalized fading models, namely κ-μ and η-μ, to characterize the fading behavior in D2D communications. Together, these models encompass many of the most widely utilized fading models in the literature such as Rayleigh, Rice (Nakagami-n), Nakagami-m, Hoyt (Nakagami-q), and One-sided Gaussian. Using stochastic geometry, we evaluate the spectral efficiency and outage probability of D2D networks under generalized fading conditions and present new insights into the tradeoffs between the reliability, rate, and mode selection. Through numerical evaluations, we also investigate the performance gains of D2D networks and demonstrate their superiority over traditional cellular networks.

Emerging cellular technologies such as those proposed for use in 5G communications will accommodate a wide range of usage scenarios with diverse link requirements. This will necessitate operation over a versatile set of wireless channels ranging from indoor to outdoor, from line-of-sight (LOS) to non-LOS, and from circularly symmetric scattering to environments which promote the clustering of scattered multipath waves. Unfortunately, many of the conventional fading models lack the flexibility to account for such disparate signal propagation mechanisms. To bridge the gap between theory and practical channels, we consider κ-μ shadowed fading, which contains as special cases the majority of the linear fading models proposed in the open literature. In particular, we propose an analytic framework to evaluate the average of an arbitrary function of the signal-to-noise-plus-interference ratio (SINR) over κ-μ shadowed fading channels by using an orthogonal expansion with tools from stochastic geometry. Using the proposed method, we evaluate the spectral efficiency, moments of the SINR, and outage probability of a K-tier heterogeneous cellular network with K classes of base stations (BSs), differing in terms of the transmit power, BS density, shadowing, and fading characteristics. Building upon these results, we provide important new insights into the network performance of these emerging wireless applications while considering a diverse range of fading conditions and link qualities.

Device-to-device (D2D) communications are recognized as a key component of future wireless networks which will help to improve spectral efficiency and network densification simultaneously. In order to guarantee a quality of service (QoS) to the cellular links, the transmit power of the D2D nodes needs to be restricted, which has lead to a poor link quality over D2D transmission. One viable option to improve the D2D link quality is incorporating cooperative relays into D2D networks. However most of the existing published work in relay assisted D2D networks has assumed that relay nodes cooperate spontaneously. This cannot always be guaranteed and we take this into account by considering a fundamental model on which donation-based cooperation depends. In particular we model relay cooperation as a donation game based on social comparison and characterize cooperation probability in an evolutionary context. When applying this model we evaluate the outage and capacity of relay assisted D2D network using a stochastic geometric framework.

Future wireless networks are expected to achieve higher data rates and ubiquitous coverage by seamless cooperation among diverse network technologies. However, it also increases the risk of co-channel interference and introduces the possibility of correlation in the aggregated interference. To address this problem, we adopt a stochastic geometry based approach by assuming that the interfering nodes are randomly distributed according to a Poisson point process (PPP). Using this approach, we derive closed-form expressions for the successful transmission probability and local delay in relay networks with correlated interference. Additionally, we find the optimal transmission probability p that jointly maximizes the successful transmission probability and minimizes the local delay. Numerical results are provided to confirm that the proposed joint optimization strategy achieves significant performance gains compared to conventional schemes.

Future mobile networks are converging toward heterogeneous multitier networks, where macro-, pico-, and femtocells are randomly deployed based on user demand. A popular approach for analyzing heterogeneous networks (HetNets) is to use stochastic geometry and treat the location of BSs as points distributed according to a homogeneous Poisson point process (PPP). However, a PPP model does not provide an accurate model for the interference when nodes are clustered around highly populated areas. This motivates us to find better ways to characterize the aggregate interference when transmitting nodes are clustered following a Poisson cluster process (PCP) while taking into consideration the fact that BSs belonging to different tiers may differ in terms of transmit power, node densities, and link reliabilities. To this end, we consider K-tier HetNets and investigate the outage probability, the coverage probability, and the average achievable rate for such networks. We compare the performance of HetNets when nodes are clustered and otherwise. By comparing these two types of networks, we conclude that the fundamental difference between a PPP and a PCP is that, for a PPP, the number of simultaneously covered mobiles and the network capacity linearly increase with K. However, for a PCP, the improvements in the coverage and the capacity diminish as K grows larger, where the curves saturate at some point. Based on these observations, we determine the scenarios that jointly maximize the average achievable rate and minimize the outage probability.

In this paper, we propose an adaptive network coding scheme for cognitive relay networks comprising multiple secondary sources communicating with a common destination in the presence of multiple primary users. Conventional network coding schemes developed for cognitive radio networks normally use global encoding kernels to achieve the minimum end-to-end outage probability. Finding the global kernel is computationally inefficient especially when the number of nodes in a network changes. To this end, we propose a network coding scheme that evenly groups the codewords into multiple subsets, linearly combines the network encoded codewords over the reduced subset, and dynamically adjusts the encoding set size to minimize the end-to-end outage probability. An advantage of the proposed network coding scheme is that it achieves lower end-to-end outage probability as compared to the conventional network coding scheme over the whole signal-to-noise ratio (SNR) range with a small additional overhead. We derive closed-form expressions for the link outage probability while taking the interference constraints into consideration. We also derive the exact end-to-end outage probability of the proposed scheme and compare its performance to that of conventional fixed network coding. We show that the proposed scheme provides a trade-off between the probability of relay cooperation and network coding gain. We demonstrate through numerical examples that the proposed adaptive network coding scheme achieves gains of more than 4 dB at a target outage probability of 10-2as compared to conventional fixed network coding schemes.

Future mobile networks are converging towards being heterogeneous, owing to the co-existence of multi-tier networks within the same geographical area, including macro, pico- and femto-cells. The deployment of such networks is generally based on user demand, which is irregular and random, implying that the deployment of base stations (BSs) is random as well. As a result, analyzing the communication protocols over heterogeneous networks (HetNets) is very challenging. A popular approach is to use stochastic geometry and treat the location of the BSs as points distributed according to a spatial Point Process. Most of the related work on the interference modeling normally assumes homogeneous Poisson point process (PPP). This assumption holds when the nodes are uniformly distributed in space, such as sensor networks or ad-hoc networks. Due to geographical factors, it may be the case for mobile users to cluster around highly populated cities and the PPP assumption does not provide an accurate model for the interference in these conditions. This motivates us to find better ways to characterize the aggregate interference when the transmitting nodes are clustered following a Poisson Cluster Process (PCP). Furthermore, the BSs belonging to different tiers may differ in terms of the transmit power, the node densities, and their link reliabilities. To this end, we consider K-tier HetNets, where, by using the Laplace transform approach, we characterize the aggregate interference at a given destination as a heavy-tailed distribution. Using the derived distribution, we investigate the probability of outage and coverage for such networks. Due to some difficulty in obtaining closed-form expressions for these measures, we derive tight bounds and verify that through numerical examples. We also compare the performance of HetNets when the nodes are clustered and otherwise. We observe that using the PPP results in larger success probability, but using the clustered process results in a larger coverage probability. We also observe that there is an optimal intensity, i.e., number of nodes, that achieves the maximum coverage probability for the given SINR (signal-to-interference-plus-noise ratio) threshold.

We consider network coded cooperation for cognitive relay networks. The primary system comprises multiple sources and multiple destinations, whereas the secondary system comprises multiple sources, multiple relays and a single destination. We derive a closed form expression for the end-to-end outage probability for the secondary system while assuming the presence of interference constraints between the two sub-systems. Based on the diversity order analysis, we propose a framework for adaptive network coding. The proposed scheme involves using a small encoding set size for low link quality and a large encoding set for good link quality. Having a small set size increases the probability of having relay cooperation, which comes at the expense of some loss in coding gain, whereas using a large encoding set size decreases the probability of having relay cooperation, but achieves some network coding gains. Therefore, there is a fundamental trade-off between the probability of relay cooperation and the achievable network coding gains. Using numerical results, we show that the proposed adaptive network coding achieves up to 5 dB gain at target outage 10-3as compared to conventional fixed network coding schemes.

We consider random network coding in noisy large-scale multiple-access relay networks in which the source packets that are correctly received at a relay are linearly combined with randomly chosen coefficients and forwarded to the destination. We derive the union bound on the average probability of decoding error at the destination with the maximum likelihood decoding, averaged over all possible node locations and relay encoding rules. The union bound provides an upper bound to the probability of decoding error with the best network coding scheme and enables us to determine the error exponent. From the error exponent, we determine the reliability-rate tradeoff and the achievable rate at the high node density regime. The high node density analysis is useful for understanding the performance of large-scale multiple access relay networks. The energy saving at a source node offered by the energy expenditure at a relay node and the optimum constellation size that minimizes the energy per information bit (Eb/N0) are investigated as a function of reliability, rate, and node density. The effect of MIMO transmit modes at the relay nodes, when they are equipped with multiple antennas, on the network-wide reliability-rate tradeoff is investigated. The insight provided by the analysis is useful for understanding of the fundamental limit and tradeoffs in large-scale multiple-access relay networks with network coding.

We consider a multiple-access relay network where multiple source nodes send independent packets to a common destination with the assistance of multiple relay nodes. We assume that the relay nodes are equipped with multiple antennas and are allowed to choose either spatial multiplexing (SM) or transmit diversity (TD) in sending network coded packets. We verify the performance limit of conventional MIMO network coding and propose two optimization schemes to overcome this limit. To this end, we develop an integrated design methodology that jointly optimizes the redundancy offered by network coding at the relays and channel coding at the sources as well as the spatial redundancy offered by multiple antennas in order to minimize the end-to-end outage probability. We show that such joint optimization can provide a significant energy saving.

We design a multiple-access relay network with multiple sources and relays via network coded cooperation where the radio resources in three dimensions: time, space and user are jointly optimized. For this, we derive the decoding error probability of two MIMO modes and develop an optimal MIMO mode selection and power allocation strategy for multiple sources and relays, so as to minimize the decoding error probability. Numerical results show that the optimal MIMO mode selection and power allocation strategy provides a significant gain of 25dB in SNR, and this SNR gain increases as the number of relays increases.

We consider a multiple access relay network where multiple source nodes send independent packets to a common destination with the assistance from a relay node. We assume that the relay node is equipped with multiple antennas and is allowed to choose either spatial multiplexing (SM) or beam forming mode as transmit diversity (TD). We develop an integrated design methodology for MIMO transmission, network coding at the relay node, and channel coding rate at the source nodes that minimizes the end-to-end outage probability.

We present a new architecture for multi-antenna receivers that cancels the co-channel interference (CCI) using a single radio frequency (RF) and baseband (BB) chain, while still achieving nearly the same bit error rate that can be provided by the conventional receiver architecture requiring multiple RF/BB chains. The proposed receiver architecture enables multiple transmitter-receiver pairs to simultaneously communicate in the same frequency band without additional bandwidth, thereby increasing the spectral efficiency or capacity, with significantly reduced receiver complexity and power consumption.