62 research outputs found
On Modeling Heterogeneous Wireless Networks Using Non-Poisson Point Processes
Future wireless networks are required to support 1000 times higher data rate,
than the current LTE standard. In order to meet the ever increasing demand, it
is inevitable that, future wireless networks will have to develop seamless
interconnection between multiple technologies. A manifestation of this idea is
the collaboration among different types of network tiers such as macro and
small cells, leading to the so-called heterogeneous networks (HetNets).
Researchers have used stochastic geometry to analyze such networks and
understand their real potential. Unsurprisingly, it has been revealed that
interference has a detrimental effect on performance, especially if not modeled
properly. Interference can be correlated in space and/or time, which has been
overlooked in the past. For instance, it is normally assumed that the nodes are
located completely independent of each other and follow a homogeneous Poisson
point process (PPP), which is not necessarily true in real networks since the
node locations are spatially dependent. In addition, the interference
correlation created by correlated stochastic processes has mostly been ignored.
To this end, we take a different approach in modeling the interference where we
use non-PPP, as well as we study the impact of spatial and temporal correlation
on the performance of HetNets. To illustrate the impact of correlation on
performance, we consider three case studies from real-life scenarios.
Specifically, we use massive multiple-input multiple-output (MIMO) to
understand the impact of spatial correlation; we use the random medium access
protocol to examine the temporal correlation; and we use cooperative relay
networks to illustrate the spatial-temporal correlation. We present several
numerical examples through which we demonstrate the impact of various
correlation types on the performance of HetNets.Comment: Submitted to IEEE Communications Magazin
A Stochastic Geometric Analysis of Device-to-Device Communications Operating over Generalized Fading Channels
Device-to-device (D2D) communications are now considered as an integral part
of future 5G networks which will enable direct communication between user
equipment (UE) without unnecessary routing via the network infrastructure. This
architecture will result in higher throughputs than conventional cellular
networks, but with the increased potential for co-channel interference induced
by randomly located cellular and D2D UEs. The physical channels which
constitute D2D communications can be expected to be complex in nature,
experiencing both line-of-sight (LOS) and non-LOS (NLOS) conditions across
closely located D2D pairs. As well as this, given the diverse range of
operating environments, they may also be subject to clustering of the scattered
multipath contribution, i.e., propagation characteristics which are quite
dissimilar to conventional Rayeligh fading environments. To address these
challenges, we consider two recently proposed generalized fading models, namely
and , to characterize the fading behavior in D2D
communications. Together, these models encompass many of the most widely
encountered and utilized fading models in the literature such as Rayleigh, Rice
(Nakagami-), Nakagami-, Hoyt (Nakagami-) and One-Sided Gaussian. Using
stochastic geometry we evaluate the rate and bit error probability of D2D
networks under generalized fading conditions. Based on the analytical results,
we present new insights into the trade-offs between the reliability, rate, and
mode selection under realistic operating conditions. Our results suggest that
D2D mode achieves higher rates over cellular link at the expense of a higher
bit error probability. Through numerical evaluations, we also investigate the
performance gains of D2D networks and demonstrate their superiority over
traditional cellular networks.Comment: Submitted to IEEE Transactions on Wireless Communication
Deep Learning Based Proactive Optimization for Mobile LiFi Systems with Channel Aging
This paper investigates the channel aging problem of mobile light-fidelity
(LiFi) systems. In the LiFi physical layer, the majority of the optimization
problems for mobile users are non-convex and require the use of dual
decomposition or heuristics techniques. Such techniques are based on iterative
algorithms, and often, cause a high processing delay at the physical layer.
Hence, the obtained solutions are no longer optimal since the LiFi channels are
evolving. In this paper, a proactive-optimization (PO) approach that can
alleviate the LiFi channel aging problem is proposed. The core idea is to
design a long-short-term-memory (LSTM) network that is capable of predicting
posterior positions and orientations of mobile users, which can be then used to
predict their channel coefficients. Consequently, the obtained channel
coefficients can be exploited to derive near-optimal transmission-schemes prior
to the intended service-time, which enables real-time service. Through various
simulations, the performance of the designed LSTM model is evaluated in terms
of prediction error and time, as well as its application in a practical LiFi
optimization problem
Resource Allocation in Heterogeneous Small-Cell Networks with Interference Avoidance Admission
Abstract-In this paper, we consider a heterogenous small-cell network (HSCNet) with co-channel deployment and investigate the throughput performance of the proposed admission control algorithm based on a new received interference constraint at the small-cell base station (SCBS) nodes. In the considered co-channel heterogenous network, by utilizing the proposed admission algorithm based on the amount of received interference generated at the neighboring macro users, the new user is admitted at the small-cell network (SCNet) while ensuring that no harmful interference is caused to the adjacent macro users. For the proposed admission algorithm, we investigate the achievable effective capacity performance of the SCNet subject to maximizing the throughput performance of the SCNet. Finally, we sustain our theoretical analysis by numerical results
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