1,627 research outputs found
Distributed SIR-Aware Opportunistic Access Control for D2D Underlaid Cellular Networks
In this paper, we propose a distributed interference and channel-aware
opportunistic access control technique for D2D underlaid cellular networks, in
which each potential D2D link is active whenever its estimated
signal-to-interference ratio (SIR) is above a predetermined threshold so as to
maximize the D2D area spectral efficiency. The objective of our SIR-aware
opportunistic access scheme is to provide sufficient coverage probability and
to increase the aggregate rate of D2D links by harnessing interference caused
by dense underlaid D2D users using an adaptive decision activation threshold.
We determine the optimum D2D activation probability and threshold, building on
analytical expressions for the coverage probabilities and area spectral
efficiency of D2D links derived using stochastic geometry. Specifically, we
provide two expressions for the optimal SIR threshold, which can be applied in
a decentralized way on each D2D link, so as to maximize the D2D area spectral
efficiency derived using the unconditional and conditional D2D success
probability respectively. Simulation results in different network settings show
the performance gains of both SIR-aware threshold scheduling methods in terms
of D2D link coverage probability, area spectral efficiency, and average sum
rate compared to existing channel-aware access schemes.Comment: 6 pages, 6 figures, to be presented at IEEE GLOBECOM 201
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
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
EXIT-charts-aided hybrid multiuser detector for multicarrier interleave-division multiple access
A generically applicable hybrid multiuser detector (MUD) concept is proposed by appropriately activating different MUDs in consecutive turbo iterations based on the mutual information (MI) gain. It is demonstrated that the proposed hybrid MUD is capable of approaching the optimal Bayesian MUD's performance despite its reduced complexity, which is at a modestly increased complexity in comparison with that of the suboptimum soft interference cancellation (SoIC) MU
- …