Explicit Modelling and Performance Analysis of Cell Group Selection With Backhaul-Aware Biasing

Hybrid satellite-terrestrial relay networks (HSTRNs) which exploit relay cooperative technology in the integrated system of satellites and terrestrial base stations (BSs) are expected to offer enhanced transmission performance benefited from the spatial gain of independent fading paths. To enable multi-user accessing and resource sharing among users, non-orthogonal multiple access (NOMA) technique can be applied in HSTRNs. In this paper, we consider a NOMA-based HSTRN where a satellite transmits signals to a relay base station (RBS) and a destination ground station (DGS), and RBS also serves a cellular user (CU) and DGS simultaneously by leveraging NOMA scheme. We assume that the satellite shares spectrum with a number of cellular small BSs (SBSs), hence SBSs will cause co-channel interference (CCI) to DGS, RBS and CU. We examine the transmission characteristics of the satellite and terrestrial links, and derive the closed-form expressions of the outage probability (OP) and average symbol error rate (ASER) of DGS and CU, respectively. Specially, we consider the effect of perfect and imperfect successive interference cancellation (SIC) on the ASER performance of CU. Then, considering the stochastic characteristics of satellite links and CCI, we analyze the mean energy efficiency (MEE) of the satellite and propose an optimal power allocation scheme which maximizes the MEE of the satellite and achieves system performance optimization. Simulation results demonstrate the effectiveness of the theoretical analysis and the proposed power allocation scheme.

Coded caching is able to exploit accumulated cache size in a wireless network by storing fractions of files in each node. In this work, we model, analyze, and optimize coded caching in cache-enabled small-cell networks (SCNs). We first propose a content delivery framework for coded caching, where multiple small base stations (SBSs) that cache different coded packets of a desired file transmit simultaneously upon a user request and the packets are decoded using successive interference cancellation (SIC)-based receiver. A closed-form expression for the successful transmission probability with SIC receiver in the high signal-to-noise ratio (SNR) region is derived by using stochastic geometry. We next optimize the cache placement to maximize the average fraction of offloaded traffic by the cache-enabled SBSs. The coded cache placement problem is formulated as a multiple-choice knapsack problem (MCKP). By exploiting the analytic properties of the average traffic offload fraction, we propose a greedy but optimal algorithm. Numerical results show our proposed coded caching scheme facilitated by simultaneous transmission and SIC-based receiving significantly outperforms uncoded caching. In addition, the coded caching only needs to split each file into a small number of fragments to approach an ideal performance.

We consider a network model where small base stations (SBSs) have caching capabilities as a means to alleviate the backhaul load and satisfy users’ demand. The SBSs are stochastically distributed over the plane according to a Poisson point process (PPP) and serve their users either (i) by bringing the content from the Internet through a finite rate backhaul or (ii) by serving them from the local caches. We derive closed-form expressions for the outage probability and the average delivery rate as a function of the signal-to-interference-plus-noise ratio (SINR), SBS density, target file bitrate, storage size, file length, and file popularity. We then analyze the impact of key operating parameters on the system performance. It is shown that a certain outage probability can be achieved either by increasing the number of base stations or the total storage size. Our results and analysis provide key insights into the deployment of cache-enabled small cell networks (SCNs), which are seen as a promising solution for future heterogeneous cellular networks.

Both non-orthogonal multiple access (NOMA) and ultra-dense network (UDN) are promising technologies in future wireless networks. However, considering the overlapped coverage of small base stations (SBSs) and the spectrum sharing with NOMA, interference management (IM) becomes a more complex and fundamental problem. Moreover, considering the massive SBSs and dynamic network conditions in UDN, more efficient mechanisms need to be designed to deal with the IM issue. Thus, we propose a distributed self-optimizing interference management approach to address both the intra-cell interference caused by NOMA and the inter-cell interference among dense deployed SBSs. Aiming to minimize the interference and guarantee the users’ requirements, we mathematically formulate the joint resource allocation and user selection problem with consideration of the diverse user requirements, complicated interference topology, and limited resources. Furthermore, we consider the imperfections of successive interference cancellation at receivers for separating and decoding superimposed signals and analyze the impacts of residual interference and outage probability in NOMA-based UDNs. For tractability purpose, we introduce interference graph and satisfaction game theory and propose distributed algorithms to solve the problem. Simulation results show that interference can be reduced significantly in UDNs with NOMA compared with the traditional IM approaches.

Performance analysis of coverage-centric heterogeneous cellular networks using dual-slope path loss model

Cellular operators have concluded that small cell deployments are a very cost-effective and quick solution to meet the ever growing demands on capacity and coverage in cellular networks for indoor and outdoor environments. While traveling in public transit vehicles, cellular subscribers usually experience poor signal reception and low bandwidth. We hence consider deploying small cells onboard (i.e., mobile small cells) such vehicles. This should enhance subscribers' quality of experience (QoE). We consider a Small Base Station (SBS) mounted in a public transit bus (i.e., mobile SBS) to serve onboard users. The mobile SBS aggregates users' traffic to and from the macroBSs. To further extract the underlying rich multipath-Doppler diversities resulting from the fast mobility and the associated selective fading channel, a pre-coded transmission is deployed in the mobile SBS.We examine the achievable gain from enabling aggregation through mobile SBS in terms of the outage probability. We derive a tight-bound closed-form expression for the outage probability in the downlink (DL). Our results indicate that significant gain in outage probability and coverage are achievable.

Outage analysis for multi-radio heterogeneous networks in the presence of aerial jammers

As a large number of small base stations have been deployed in dense heterogeneous networks, the increasing cost of energy has become a challenging issue. In this paper, we propose an optimized clustering-based sleep strategy to release the power consumption and interference in the system. First, we group the small base stations with large interference as a cluster in which the binary particle swarm optimization (BPSO) algorithm is used to formulate a sleep strategy for small base stations in the clusters. Since the small base stations between clusters have less interference, the whole spectrum resources can be shared in all clusters and the orthogonal spectrum resources are allocated for the small base stations in the same cluster. The strategy effectively restrains the co-tier interference in the system. Furthermore, a separate sleep strategy is applied for each cluster to improve the sleep and activation efficiency of the small base stations. The simulation results show that the proposed sleep strategy can improve the satisfaction of user equipment and reduce the power consumption. As the increasing number of user equipment, the outage probability of the system can be reduced effectively.

To alleviate the backhaul congestion in future hybrid heterogenous networks, this paper investigates the potential benefits of implementing storages in small base stations (SBSs) operating at frequency range 2 (FR2) bands that co-exist with a tier of massive multiple input multiple output (MIMO) macro BSs (MBSs) operating at frequency range 1 (FR1) bands. We develop a unified analytical framework and derive theoretical bounds for such a cache-enabled FR1-FR2 hybrid network under limited backhaul scenario to analyze the exact and approximate latency, average success probability of file delivery, and average data rate considering two open-access user association policies: i) location-based and ii) content-based association. Numerical results demonstrate that wireless edge caching (WEC) can improve the performance of hybrid wireless networks, albeit certain trade-offs, e.g., increasing cache-enabled SBSs cannot always improve the network performance and there exists an optimal SBS density that provides the best latency and throughput performance. Furthermore, we compare the performance of the network with respect to other key network design parameters such as cache size, content popularity, backhaul capacity, and blockages for both the user associations. Our results show that latency under content-based user association is less than that of location-based user association, and although the difference in the average rates under the two user associations is not obvious, content-based association can extricate more backhaul capacity and thus reduce installation cost significantly.

Uplink/downlink (UL/DL) decoupled access has emerged as a promising paradigm for heterogeneous cellular vehicle-to-everything (C-V2X) networks in beyond 5G (B5G) and 6G systems. In multi-operator scenarios, wireless service provider (WSP) selection becomes critical for vehicles to ensure communication quality while minimizing costs. This paper investigates the performance analysis and WSP selection problem in UL/DL decoupled access C-V2X networks. We derive tractable expressions for spectral efficiency of both UL and DL using stochastic geometry, considering three decoupled access cases where UL and DL independently associate with macro base stations (MBSs) or small base stations (SBSs). We formulate a hierarchical game framework combining evolutionary game for vehicle WSP selection and non-cooperative game for WSP bandwidth allocation. An evolutionary game algorithm is proposed to reach the equilibrium, and the uniqueness of Nash equilibrium in bandwidth allocation is proved. Extensive simulations validate the analytical results and demonstrate the convergence and stability of the proposed game framework.

Future mobile networks are converging toward heterogeneous multi-tier networks, where various classes of base stations (BS) are deployed based on user demand.So it is quite necessary to utilize the BSs resources rationally when BSs are sufficient.In this paper, we develop a more realistic model that fully considering the inter-tier dependence and the dependence between users and BSs, where the macro base stations (MBSs) are distributed according to a homogeneous Poisson point process (PPP) and the small base stations (SBSs) follows a Matern cluster process (MCP) whose parent points are located in the positions of the MBSs in order to offload the users from the over-loaded MBSs.We also assume the users are just randomly located in the circles centered at the MBSs.Under this model, we derive the association probability and the average ergodic rate by stochastic geometry.An interesting result that the density of MBS and the radius of the clusters jointly affect the association probabilities in a joint form is obtained.We also observe that using the clustered SBSs results in aggressive offloading compared with previous cellular networks.

Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestion at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability in multi-channel cache-enabled 5G networks with both unicast/multicast capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing the opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. An optimization framework for file caching is then developed to maximize the hit probability. To this end, a simple concave approximation for the hit probability is proposed, which highly reduces the optimization complexity and leads to a closed-form solution. The sub-optimal solution is benchmarked against two widely employed caching distribution schemes, namely, uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the caching strategy should be adapted to the network parameters and capabilities. For instance, diversifying file caching according to the Zipf distribution is better in multicast systems with large number of channels. However, when the number of channels is low and/or the network is restricted to unicast transmissions, it is better to confine caching to the most popular files only.

Due to irregular deployment of small base stations (SBSs), the interference in cognitive heterogeneous networks (CHNs) becomes even more complex; in particular, the uncertainty of spectrum mobility aggravates the interference context. In this case, how to analyze system capacity to obtain a closed-form expression becomes a crucial problem. In this paper we employ stochastic methods to formulate the capacity of CHNs and achieve a closed-form expression. By using discrete-time Markov chains (DTMCs), the spectrum mobility with respect to the arrival and departure of macro base station (MBS) users is modeled. Then an integral method is proposed to derive the interference based on stochastic geometry (SG). Also, the effect of sensing accuracy on network capacity is discussed by concerning false-alarm and miss-detection events. Simulation results are illustrated to show that the proposed capacity analysis method for CHNs can approximate the conventional sum methods without rigorous requirement for channel station information (CSI). Therefore, it turns out to be a feasible and efficient way to capture the network capacity in CHNs.

In Heterogeneous networks (HetNets), the power difference between macro base stations (MBSs) and small base stations (SBSs) causes severe load unbalance. Therefore, cell range expansion (CRE) is proposed as an effective method to extend the coverage of SBSs and achieve balanced utilization of BSs. However, the downlink (DL) quality for offloaded user equipment (UE) cannot be guaranteed. In this paper, a traffic-aware user association scheme is proposed in HetNets. Distinct association biases are applied to different UEs according to their requirements. System performance of the proposed scheme is analyzed using the tool of stochastic geometry. The results show that the proposed scheme can improve DL throughput by enhancing the rate coverage of UEs, meanwhile signal-to-interference-plus-noise ratio (SINR) requirement with low data rate demand UEs is ensured. Moreover, the optimal association bias, which maximizes DL throughput, can be derived through particle swarm optimization (PSO), and it changes with different densities of BSs and UEs.

In order to minimize electric grid power consumption, energy harvesting from ambient RF sources is considered as a promising technique for wireless charging of low-power devices. To illustrate the design considerations of RF-based ambient energy harvesting networks, this article first points out the primary challenges of implementing and operating such networks, including non-deterministic energy arrival patterns, energy harvesting mode selection, energy-aware cooperation among base stations (BSs), etc. A brief overview of the recent advancements and a summary of their shortcomings are then provided to highlight existing research gaps and possible future research directions. To this end, we investigate the feasibility of implementing RF-based ambient energy harvesting in ultra-dense small cell networks (SCNs) and examine the related trade-offs in terms of the energy efficiency and signal-to-interference-plus-noise ratio (SINR) outage probability of a typical user in the downlink. Numerical results demonstrate the significance of deploying a mixture of on-grid small base stations (SBSs)~(powered by electric grid) and off-grid SBSs~(powered by energy harvesting) and optimizing their corresponding proportions as a function of the intensity of active SBSs in the network.