Low Earth orbit (LEO) satellite networks have become pivotal in modern communication systems, offering ubiquitous connectivity for direct-to-satellite (DtS) service in remote and underserved regions. To overcome the limited power of handheld devices, cooperative communication offers an opportunity to enhance connectivity, e.g., coverage probability, by leveraging multiple satellites to mitigate interference and improve signal quality. To this end, this paper proposes a stochastic geometry-based framework to analyze the performance of large-scale cooperative satellite networks. We model the satellites and interfering ground users as Poisson Point Processes (PPPs) and consider a typical user served by its closest subset of visible satellites. The received uplink signals at the serving satellites are combined using the maximum ratio combining (MRC) technique. We derive the coverage probability, accounting for the correlation among serving distances and interference from neighboring devices. Our analytical results, validated via Monte Carlo simulations, demonstrate a 33% increase in coverage probability when the number of cooperating devices doubles at an SINR threshold of -10 dB, offering key design insights for optimizing cooperative LEO satellite networks.

Feedback transmissions are used to acknowledge correct packet reception, trigger erroneous packet re-transmissions, and adapt transmission parameters (e.g., rate and power). Despite the feedback paramount role in establishing reliable communication links, the majority of the literature overlooks its impact by assuming genie-aided systems with flawless and instantaneous feedback. However, this idealistic assumption is no longer valid for large-scale Internet of Things (IoT) networks, characterized by energy-constrained devices, susceptible to interference, and serving delay-sensitive applications. Furthermore, feedback-free operation is necessitated for IoT receivers with stringent energy constraints. In this context, this paper explicitly accounts for the impact of feedback in energy-constrained delay-sensitive large-scale IoT networks. We consider a time-slotted system with closed-loop and open-loop rate adaptation schemes, where packets are fragmented to operate at a reliable transmission rate satisfying packet delivery deadlines. In the closed-loop scheme, the delivery of each fragment is acknowledged through an error-prone feedback channel. The open-loop scheme has no feedback mechanism, and hence, a predetermined fragment repetition strategy is employed to improve transmission reliability. Using stochastic geometry and queueing theory, we develop a novel spatiotemporal framework for both schemes to quantify the impact of feedback on network performance in terms of transmission reliability, latency, and energy consumption.

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.

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 congestions at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability of a cache-enabled multicast 5G network with SBS multi-channel capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. The optimal caching distribution to maximize the hit probability is then computed. The performance and trade-offs of the derived optimal caching distributions are then assessed and compared with two widely employed caching distribution schemes, namely uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the Zipf caching almost optimal only in scenarios with large number of available channels and large cache sizes.

Cache-enabled base station (BS) densification, denoted as a fog radio access network (F-RAN), is foreseen as a key component of 5G cellular networks. F-RAN enables storing popular files at the network edge (i.e., BS caches), which empowers local communication and alleviates traffic congestions at the core/backhaul network. The hitting probability, which is the probability of successfully transmitting popular files request from the network edge, is a fundamental key performance indicator (KPI) for F-RAN. This paper develops a scheduling aware mathematical framework, based on stochastic geometry, to characterize the hitting probability of F-RAN in a multi-channel environment. To this end, we assess and compare the performance of two caching distribution schemes, namely, uniform caching and Zipf caching. The numerical results show that the commonly used single channel environment leads to pessimistic assessment for the hitting probability of F-RAN. Furthermore, the numerical results manifest the superiority of the Zipf caching scheme and quantify the hitting probability gains in terms of the number of channels and cache size.