In this paper, we investigate a hybrid caching strategy maximizing the success transmission probability (STP) in a millimeter wave (mmWave) cache-enabled network. First, we derive theoretical expressions of the STP by utilizing stochastic geometry, then we consider the maximization of the STP by optimizing the design parameters. Considering the optimality structure of the NP-hard problem, the original problem is transformed into a multi-choice knapsack problem (MCKP). Finally, we investigate the impact of key network parameters on the STP. Numerical results demonstrate the superiority of the proposed caching strategy over the conventional caching strategies in the mmWave cache-enabled networks.

In recent years, optimization of the successful transmission probability (STP) in wireless cache-enabled networks has been studied extensively. However, few works have examined the real-time performance of the cache-enabled networks. In this paper, we investigate the performance of the cache-enabled networks with real-time guarantees by adopting age of information (AoI) as the metric to characterize the timeliness of the delivered information. We establish a spatial-temporal model by utilizing stochastic geometry and queueing theory which captures both the temporal traffic dynamics and the interferers’ geographic distribution. Under the random caching framework, we achieve the closed-form expression of AoI by adopting the maximum average received power criterion for the user association. Finally, we formulate a convex optimization problem for the minimization of the Peak AoI(PAoI) and obtain the optimal caching probabilities by utilizing the Karush-Kuhn-Tucker (KKT) conditions. Numerical results demonstrate that the random caching strategy is a better choice than both the most popular caching (MPC) and uniform caching (UC) strategies when it comes to improving the real-time performance for the cached files as well as maintaining the file diversity.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for the next generation networks. By utilizing tools from stochastic geometry, we develop a meta distributed-based analytical framework to study the effect of the large-scale deployment of the RIS on the performance of a millimeter wave (mmWave) cellular network. Specifically, the locations of the base stations (BSs) are modeled as Poisson point processes (PPPs). In addition, the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. By considering the randomness of the locations and orientations of the RISs and the particular characteristics of mmWave communications, we provide a statistical characterization of the path loss for the BSs and RISs and derive the analytical expressions for the k-th moment of the conditional success probability, the area spectral efficiency and the energy efficiency. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs.

A fine-grained analysis of network performance is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as a Poisson point process (PPP). By utilizing stochastic geometry and queueing theory, we characterize the spatial and temporal randomness while the special characteristics of mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming, are incorporated into the analysis. We derive the moments of the conditional successful transmission probability (STP). By taking the temporal random arrival of traffic into consideration, an equation on the meta distribution is formulated and the meta distribution can be obtained in a recursive manner. The numerical results reveal the impact of the key network parameters, such as the SINR threshold and the blockage parameter, on the network performance.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for millimeter wave (mmWave) networks. In this paper, we utilize tools from stochastic geometry to study the performance of a RIS-assisted mmWave cellular network. Specifically, the locations of the base stations (BSs) and the midpoints of the blockage are modeled as two independent Poisson point processes (PPPs), where the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. The particular characteristics of mmWave communications, i.e., directional beamforming and different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) propagation, are incorporated into our analysis. We derive analytical expressions for the success probability and the area spectral efficiency. The success probability under the special case where the blockage parameter is sufficiently small is also derived. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs. In addition, the tradeoff between the BS and RIS densities is investigated and the results show that the RISs can indeed enable the traditional networks to improve the success probability, especially for the cell-edge region, with limited power consumption.

A fine-grained analysis of the cache-enabled networks is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference ratio for the cache-enabled networks where the locations of the base stations are modeled as a Poisson point process. With the application of the random caching and the random discontinuous transmission schemes, we derive the moments of the conditional successful transmission probability, the exact meta distribution and its beta approximation by utilizing stochastic geometry. The closed-form expressions of the mean and variance of the local delay (i.e., the jitter) are also derived. We then consider the maximization of the mean successful transmission probability and the minimization of the average system transmission delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of successful transmission probability, successful transmission probability variance, meta distribution, mean local delay and jitter.

In this paper, we focus on the local delay for the cache-enabled networks where the locations of the base stations (BSs) are modeled as a Poisson point process (PPP). With the application of the random caching and the random discontinuous transmission (DTX) schemes, we derive the closed-form expression of the mean local delay. We then consider the minimization of the mean local delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of mean local delay.

Emergence of various types of wireless applications has brought about fast growing and diversified traffic in cellular networks. To gain a comprehensive understanding of the influences caused by the differentiated and dynamic traffic is vital for the design of the next-generation wireless networks. In this paper, we develop a mathematical framework for meta-distribution analysis by utilizing queueing theory and stochastic geometry to capture the spatial (geographical location) and temporal randomness (queue status) of traffic. We derive the${k}$-th moment of the conditional successful transmission probability (STP) and the closed-form expressions of the meta distribution for the cell-center users (CCUs) and the cell-edge users (CEUs), respectively. The results are further extended to obtain the analytical expression of the meta distribution by taking the temporal random arrival of traffic into consideration. Moreover, the mean local delays for the CCUs and CEUs are derived. Finally, the impact of key network parameters on the meta distribution and the corresponding mean local delay is investigated.

In this paper, we investigate the optimal caching policy in a K-tier millimeter wave (mmWave) cache- enabled heterogeneous network. In order to mitigate interferences, we incorporate base station (BS) idling into our analysis. Under the random caching framework, we derive the association probability for each tier as well as the successful transmission probability (STP) by utilizing stochastic geometry and taking the blockage effect into account. In addition, we adopt the gradient projection method to obtain the locally optimal caching probabilities and propose a two-stage scheme to obtain the globally optimal caching probabilities under the special case where the LOS ranges for K tiers are sufficiently large. Numerical results demonstrate the superiority of the proposed method over the conventional caching strategies such as Most Popular Content (MPC) and Uniform Caching (UC) schemes.