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Heterogeneous cloud radio access network (H-CRAN) has been deemed as a promising architecture for the fifth generation wireless networks. In H-CRAN, small remote radio heads (SRRHs) are deployed underlaying the macro remote radio head (MRRH) to boost the spectral efficiency. H-CRANs with dense RRHs suffer from severe inter-cell interference due to the random deployment of dense SRRHs, which limits its performance. In this paper, we propose an Edge-Aware RRH-Cooperation (EARC) scheme to mitigate the impact of both the cross-tier and co-tier interference. We partition the users into two groups, Cell Edge Device (CED) and Non-cell Edge Device (NED). These two groups, CED and NED, are mapped into two association modes, dual-association and single-association mode, respectively. In single-association mode, the NED associates with the RRH that provides the maximum received signal. On the other hand, in the dual-association mode, the CED associates with the two strongest RRHs where the two RRHs may belong to the same or different tier(s). Using tools from stochastic geometry, we quantify the performance gains of the proposed EARC scheme in terms of outage probability and ergodic rate. To further show the efficacy of the proposed scheme, we compare it to four schemes developed in the literature.

In this article, we study the uplink coverage and the uplink network throughput of an ultradense network (UDN) where human-type communication (HTC) users and machine-type communication (MTC) devices coexist. We employ non-orthogonal multiple access (NOMA) radio access to address the massive connectivity requirements of MTC while satisfying the high data rate requirements of HTC. To this end, stochastic geometry is utilized to model the network exploiting the inherent randomness of the proposed scenario. The enormous number of small cells in UDN along with NOMA can provide a common ground to satisfy the diverse requirements of both MTC and HTC. The distinguishing features of both UDNs and MTC are considered in modeling the network where stretched exponential path-loss (SEPL) is used to capture short-link distances. Moreover, the truncated channel inversion power control is employed in both HTC users (HTCUs) and MTC devices (MTCDs) to mitigate the uplink intercell interference. The results show the significant impact of various system parameters on the network performance. Closed-form and easy-computable expressions are derived for the considered performance metrics, and are assessed by Monte Carlo simulations.

The inevitable next era of smart living requires unprecedented advances in enabling technologies. The building blocks of this era are devices such as sensors, actuators, and Internet of Things (IoT) devices. Machine-Type Communication (MTC), also known as Machine-to-Machine (M2M) communication, constitutes the main enabling technology to support communications among such devices. The massive number of these devices and the immense amount of traffic generated by them require a paramount shift in cellular and non-cellular wireless technologies to achieve the required connectivity. Ultra-Dense Network (UDN) is intuitively one of the most convenient approaches to tackle such requirements of massive connectivity and high throughput found in MTC. In UDNs, small cells are deployed in large densities compared to Human-Type Communication Users (HTCUs) like smartphones and tablets. In a different scope, multiple access techniques also require a paradigm shift to cope with the increasing density of required connections while confined by limited resources. Recently, Non-Orthogonal Multiple Access (NOMA) has received a great attention as an efficacious candidate to enhance the network’s performance compared to traditional Orthogonal Multiple Access (OMA) techniques. For this sake, in this paper, we provide a comprehensive survey and illustrative simulation results on the application of NOMA to support MTC in a UDN environment. First, we give a brief discussion on MTC and its different applications and supporting technologies. Then, we focus our effort on investigating the main challenges facing MTC along with the existing opportunities found in both NOMA and UDN, respectively. Finally, we show via simulations the possible gains of both technologies and conclude by discussing the open problems for future research directions.

In this paper, we investigate the uplink coverage and ergodic capacity of massive Machine-Type Communication (mMTC) considering an Ultra-Dense Network (UDN) environment. In MTC, devices equipped with sensing, computation, and communication capabilities connect to the Internet providing what is known as Internet-of-Things (IoT). A dense network would provide an all-in-one solution where scalable connectivity, high capacity, and uniform deep coverage are byproducts. To account for short link distances, the path loss is modeled as stretched exponential path loss (SEPL). Moreover, the fading is modeled as a general $(\alpha -\mu)$ channel, where tractable and insightful results are derived for the Rayleigh fading special case. We consider the direct MTC access mode where mMTC nodes connect directly to the small cell. The analytical results disclose the impact of the system parameters and propagation environment parameters on the network performance. In particular, our results reveal that significant coverage enhancements and high uplink capacity are achievable at moderate cell densities, low transmission power, and moderate bandwidth. Moreover, the uplink network performance is independent of the maximum transmission power in the considered dense network scenario, allowing for longer battery lifetime of future IoT devices. The accuracy of the derived analytical results is assessed via extensive simulations.

We study the physical layer security in ultra-dense networks considering the close proximity of the users to the cells. In particular, we show the effect of the small cells’ density, the users’ density and the eavesdroppers’ density on the average secrecy rate. To this end, stochastic geometry is employed in order to provide tractable expressions for the average secrecy rate where Rician fading channels are considered in the main and the leakage links. The accuracy of the derived analytical expressions is confirmed by simulations.

In this paper, we propose a general mathematical framework to compute the average downlink rate in a multiple connectivity context considering ultra-dense network (UDN) environment. UDN is a dense small cells network featured by the high density of small cells that may exceed the density of active users. In multiple association, a user connects to $M$ base stations (BSs) that provide the maximum average received power forming a multicell . This provides the user with a “data-shower,” where the user’s traffic is split into multiple paths, which helps overcoming the capacity limitations imposed by the backhaul links. The developed framework significantly simplifies the computation of the average downlink rate of the individual connections to the cells of a multicell . Moreover, the accuracy of the mathematical framework is confirmed by extensive simulations. The simulation results show a perfect match with the numerical results computed from the mathematical framework in different combinations of the system parameters including multicell size, small cells density, active users density, pathloss exponent, and fading channel distribution of the signal link.

The exponential growth and availability of data in all forms is the main booster to the continuing evolution in the communications industry. The popularization of traffic-intensive applications including high definition video, 3-D visualization, augmented reality, wearable devices, and cloud computing defines a new era of mobile communications. The immense amount of traffic generated by today’s customers requires a paradigm shift in all aspects of mobile networks. Ultradense network (UDN) is one of the leading ideas in this racetrack. In UDNs, the access nodes and/or the number of communication links per unit area are densified. In this paper, we provide a survey-style introduction to dense small cell networks. Moreover, we summarize and compare some of the recent achievements and research findings. We discuss the modeling techniques and the performance metrics widely used to model problems in UDN. Also, we present the enabling technologies for network densification in order to understand the state-of-the-art. We consider many research directions in this survey, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment. Finally, we discuss the challenges and open problems to the researchers in the field or newcomers who aim to conduct research in this interesting and active area of research.