744 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
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges
With the rapid development of marine activities, there has been an increasing
number of maritime mobile terminals, as well as a growing demand for high-speed
and ultra-reliable maritime communications to keep them connected.
Traditionally, the maritime Internet of Things (IoT) is enabled by maritime
satellites. However, satellites are seriously restricted by their high latency
and relatively low data rate. As an alternative, shore & island-based base
stations (BSs) can be built to extend the coverage of terrestrial networks
using fourth-generation (4G), fifth-generation (5G), and beyond 5G services.
Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs.
Despite of all these approaches, there are still open issues for an efficient
maritime communication network (MCN). For example, due to the complicated
electromagnetic propagation environment, the limited geometrically available BS
sites, and rigorous service demands from mission-critical applications,
conventional communication and networking theories and methods should be
tailored for maritime scenarios. Towards this end, we provide a survey on the
demand for maritime communications, the state-of-the-art MCNs, and key
technologies for enhancing transmission efficiency, extending network coverage,
and provisioning maritime-specific services. Future challenges in developing an
environment-aware, service-driven, and integrated satellite-air-ground MCN to
be smart enough to utilize external auxiliary information, e.g., sea state and
atmosphere conditions, are also discussed
- …