2,328 research outputs found
Context-aware Cluster Based Device-to-Device Communication to Serve Machine Type Communications
Billions of Machine Type Communication (MTC) devices are foreseen to be
deployed in next ten years and therefore potentially open a new market for next
generation wireless network. However, MTC applications have different
characteristics and requirements compared with the services provided by legacy
cellular networks. For instance, an MTC device sporadically requires to
transmit a small data packet containing information generated by sensors. At
the same time, due to the massive deployment of MTC devices, it is inefficient
to charge their batteries manually and thus a long battery life is required for
MTC devices. In this sense, legacy networks designed to serve human-driven
traffics in real time can not support MTC efficiently. In order to improve the
availability and battery life of MTC devices, context-aware device-to-device
(D2D) communication is exploited in this paper. By applying D2D communication,
some MTC users can serve as relays for other MTC users who experience bad
channel conditions. Moreover, signaling schemes are also designed to enable the
collection of context information and support the proposed D2D communication
scheme. Last but not least, a system level simulator is implemented to evaluate
the system performance of the proposed technologies and a large performance
gain is shown by the numerical results
Self organization of tilts in relay enhanced networks: a distributed solution
Despite years of physical-layer research, the capacity enhancement potential of relays is limited by the additional spectrum required for Base Station (BS)-Relay Station (RS) links. This paper presents a novel distributed solution by exploiting a system level perspective instead. Building on a realistic system model with impromptu RS deployments, we develop an analytical framework for tilt optimization that can dynamically maximize spectral efficiency of both the BS-RS and BS-user links in an online manner. To obtain a distributed self-organizing solution, the large scale system-wide optimization problem is decomposed into local small scale subproblems by applying the design principles of self-organization in biological systems. The local subproblems are non-convex, but having a very small scale, can be solved via standard nonlinear optimization techniques such as sequential quadratic programming. The performance of the developed solution is evaluated through extensive simulations for an LTE-A type system and compared against a number of benchmarks including a centralized solution obtained via brute force, that also gives an upper bound to assess the optimality gap. Results show that the proposed solution can enhance average spectral efficiency by up to 50% compared to fixed tilting, with negligible signaling overheads. The key advantage of the proposed solution is its potential for autonomous and distributed implementation
Simplicial Homology for Future Cellular Networks
Simplicial homology is a tool that provides a mathematical way to compute the
connectivity and the coverage of a cellular network without any node location
information. In this article, we use simplicial homology in order to not only
compute the topology of a cellular network, but also to discover the clusters
of nodes still with no location information. We propose three algorithms for
the management of future cellular networks. The first one is a frequency
auto-planning algorithm for the self-configuration of future cellular networks.
It aims at minimizing the number of planned frequencies while maximizing the
usage of each one. Then, our energy conservation algorithm falls into the
self-optimization feature of future cellular networks. It optimizes the energy
consumption of the cellular network during off-peak hours while taking into
account both coverage and user traffic. Finally, we present and discuss the
performance of a disaster recovery algorithm using determinantal point
processes to patch coverage holes
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