97 research outputs found
Edge Caching in Dense Heterogeneous Cellular Networks with Massive MIMO Aided Self-backhaul
This paper focuses on edge caching in dense heterogeneous cellular networks
(HetNets), in which small base stations (SBSs) with limited cache size store
the popular contents, and massive multiple-input multiple-output (MIMO) aided
macro base stations provide wireless self-backhaul when SBSs require the
non-cached contents. Our aim is to address the effects of cell load and hit
probability on the successful content delivery (SCD), and present the minimum
required base station density for avoiding the access overload in an arbitrary
small cell and backhaul overload in an arbitrary macrocell. The massive MIMO
backhaul achievable rate without downlink channel estimation is derived to
calculate the backhaul time, and the latency is also evaluated in such
networks. The analytical results confirm that hit probability needs to be
appropriately selected, in order to achieve SCD. The interplay between cache
size and SCD is explicitly quantified. It is theoretically demonstrated that
when non-cached contents are requested, the average delay of the non-cached
content delivery could be comparable to the cached content delivery with the
help of massive MIMO aided self-backhaul, if the average access rate of cached
content delivery is lower than that of self-backhauled content delivery.
Simulation results are presented to validate our analysis.Comment: Accepted to appear in IEEE Transactions on Wireless Communication
Edge Cache-assisted Secure Low-Latency Millimeter Wave Transmission
In this paper, we consider an edge cache-assisted millimeter wave cloud radio
access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local
cache, which can pre-fetch and store the files requested by the actuators.
Multiple RRHs form a cluster to cooperatively serve the actuators, which
acquire their required files either from the local caches or from the central
processor via multicast fronthaul links. For such a scenario, we formulate a
beamforming design problem to minimize the secure transmission delay under
transmit power constraint of each RRH. Due to the difficulty of directly
solving the formulated problem, we divide it into two independent ones:
{\textit{i)}} minimizing the fronthaul transmission delay by jointly optimizing
the transmit and receive beamforming; {\textit{ii)}} minimizing the maximum
access transmission delay by jointly designing cooperative beamforming among
RRHs. An alternatively iterative algorithm is proposed to solve the first
optimization problem. For the latter, we first design the analog beamforming
based on the channel state information of the actuators. Then, with the aid of
successive convex approximation and -procedure techniques, a semidefinite
program (SDP) is formulated, and an iterative algorithm is proposed through SDP
relaxation. Finally, simulation results are provided to verify the performance
of the proposed schemes.Comment: IEEE_IoT, Accep
Reduced complexity multicast beamforming and group assignment schemes for multi-antenna coded caching
Abstract. In spite of recent advancements in wireless communication technologies and data delivery networks, it is unlikely that the speeds supported by these networks will be able to keep up with the exponentially increasing demand caused by the widespread adoption of high-speed and large-data applications. One appealing idea proposed to address this issue is coded caching, which is an innovative data delivery technique that makes use of the network’s aggregate cache rather than the individual memory available to each user. This proposed idea of coded caching helps boost the data rates by distributing cache material throughout the network and delivering independent content to many users at a time. Despite the original theoretical promises for large caching gains, in reality, coded caching suffers from severe bottlenecks that dramatically limit these gains. Some of these bottlenecks are requiring complex successive interference cancellation (SIC) at the receiver, exponential increase in subpacketization, applicability to a limited range of input parameters, and performance losses in low- and mid- signal to noise ratio (SNR) regimes. In this study, we present a novel coded caching scheme based on user grouping for cache-aided multi-input single-output (MISO) networks. One special property of this new scheme is its applicability to every set of input values for the user count (), transmitter-side antenna count (), and the global coded caching gain (). Moreover, for a fixed , this scheme can achieve theoretical sum-DoF optimality with no limitations. This strategy yields superior performance in terms of subpacketization when input parameters satisfy . This performance boost is enabled by the underlying user grouping structure during data delivery. However, when input parameters do not comply with , in order to guarantee symmetry of the scheme and optimal DoF, multicast and unicast messages need to be constructed using a tree diagram, resulting in excess subpacketization and transmission count. Nevertheless, the simple receiver structure without the SIC requirement not only simplifies the implementation complexity but also enables us to use state-of-the-art methods to readily design optimized transmit beamformers maximizing the achievable symmetric rate. Finally, we use numerical analysis to compare our new proposed scheme with well-known coded caching schemes in the literature
COCAM: a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning in multi-clouds environment
The evolution of the Internet of Things technology (IoT) has boosted the drastic increase in network traffic demand. Caching and multicasting in the multi-clouds scenario are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each edge cloud. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, we cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching strategy and further encompasses the observations of neighboring agents as constituents of the overall state. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
A Survey on Caching in Distributed Small Cell Networks
The exponential growth of mobile devices such as smartphones and tablets, coupled with proliferation of online social networks has considerably increased the traffic in cellular networks. In contrast to classical cellular traffic that was only based on voice and audio communications, the recent technologies have resulted in bandwidth-intensive services such as video streaming, and video conferencing increases the traffic among users. This traffic surge affects the capacity of existing wireless networks which makes it difficult to ensure the high quality-of-service (QoS) required by the cellular services. In order to handle with the limited capacity of existing cellular networks and keep up with the strict QoS requirements, in terms of data rate and delay tolerable application-specific delays, a new generation of wireless networks has emerged. To achieve the requirements of this new generation and provide efficient infrastructure support for this data deluge, several research challenges must be addressed and solved. In this paper a survey on literature about small cell networks in distributed environment is presented which focus on caching aspect to improve the performance. The related work for caching in distributed small cell networks is also presented
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