Cache-Enabled Millimeter Wave Cellular Networks With Clusters

Abstract

Wireless content caching in cellular networks is an efficient way to reduce the service delay and alleviate backhaul pressure. For the benefits of sharing spectral and storage resources, clustering in cached networks has recently attracted significant research interests. Meanwhile, since the multimedia content (e. g.., video) of caching networks may require a huge transmission rates, millimeter wave (mmWave) communication is considered to be an efficient transmission scheme for cacheenabled networks. We investigate the ergodic rate and average service delay for typical user terminal (UT) in the clustered cacheenabled small cell networks (SCN) and ultra dense networks (UDN) with mmWave channels. In SCN, each cluster consists of cache-enabled UTs, and in the UDN a cluster is formed by cacheenabled UTs and small base stations (SBSs) with non-uniform caching capacity. The clusters are assumed to be discs and content sharing is only possible within clusters through mmWave device-to-device (D2D) tier and SBS tier communications. With stochastic geometry methods, the distributions of content sharing distance and signal-to-interference-noise-ratio (SINR) of typical UT in a cluster are derived for both SCN and UDN scenarios. To minimize the average service delay in high SINR region, we provide an algorithm to jointly optimize caching scheme for SBSs and UTs. By simulations, we validate our theoretical analysis and the performance of proposed caching scheme. The numerical results also show that there exists best radius in the design of cluster for UDNs.

Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges

“…Authors in [52] proposed a method to solve the joint problem considering time RA, user association, and mmWave beamforming. In [53], using mmWave communication improve the average service delay in a cellular network.…”

Analysis and Optimization of Cache-Enabled mmWave HetNets with Integrated Access and Backhaul

“…For tractbility of analysis, the perfect beamforming is assumed between the transmitter and receiver [41]. Based on the above analysis, we can derive the the signal to interference plus noise ratio expression of a typical user from the associated SBS or the associated MBS via wireless access link at the distance r as follows.…”

Machine Learning: A Catalyst for THz Wireless Networks

“…The disadvantage of this algorithm is that it is unable to achieve the same performance as the corresponding supervised ML one. Moreover, in (Huang S. et al, 2020), the authors studied the use of extreme learning machine for jointly optimizing transmit and receive hybrid beamforming in MU-MIMO wireless systems. The algorithm requires as inputs the real and imaginary parts of the MIMO channel coefficients and returns the an optimal beamforming vectors estimation.…”

“…The main idea is to proactively transfer the content to be requested by a single of a cluster of UEs to the nearest possible BS/AP. Towards this direction, several researchers presented ML-based caching policies (Cheng et al, 2019;Jiang et al, 2019;Saputra et al, 2019;Wang X. et al, 2020;Kirilin et al, 2020;Ye et al, 2020). Specifically, in (Ye et al, 2020), the authors reported a device to primary and secondary BS clustering approach based on the requested content location in mmW ultra-dense wireless networks.…”

“…Towards this direction, several researchers presented ML-based caching policies (Cheng et al, 2019;Jiang et al, 2019;Saputra et al, 2019;Wang X. et al, 2020;Kirilin et al, 2020;Ye et al, 2020). Specifically, in (Ye et al, 2020), the authors reported a device to primary and secondary BS clustering approach based on the requested content location in mmW ultra-dense wireless networks. Moreover, in (Kirilin et al, 2020),the authors presented a reinforcement learning architecture, which increases the caching hit rate by deciding whether or not to admit a requested object into the content delivery network, and whether to evict contents, when the cache is full.…”