114 research outputs found
Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks
In this paper, we study the problem of cooperative interference management in
an OFDMA two-tier small cell network. In particular, we propose a novel
approach for allowing the small cells to cooperate, so as to optimize their
sum-rate, while cooperatively satisfying their maximum transmit power
constraints. Unlike existing work which assumes that only disjoint groups of
cooperative small cells can emerge, we formulate the small cells' cooperation
problem as a coalition formation game with overlapping coalitions. In this
game, each small cell base station can choose to participate in one or more
cooperative groups (or coalitions) simultaneously, so as to optimize the
tradeoff between the benefits and costs associated with cooperation. We study
the properties of the proposed overlapping coalition formation game and we show
that it exhibits negative externalities due to interference. Then, we propose a
novel decentralized algorithm that allows the small cell base stations to
interact and self-organize into a stable overlapping coalitional structure.
Simulation results show that the proposed algorithm results in a notable
performance advantage in terms of the total system sum-rate, relative to the
noncooperative case and the classical algorithms for coalitional games with
non-overlapping coalitions
Universal Intelligent Small Cell (UnISCell) for Next Generation Cellular Networks
Exploring innovative cellular architectures to achieve enhanced system capacity and good coverage has become a critical issue towards realizing the next generation of wireless communications. In this context, this paper proposes a novel concept of Universal Intelligent Small Cell (UnISCell) for enabling the densification of the next generation of cellular networks. The proposed novel concept envisions an integrated platform of providing a strong linkage between different stakeholders such as
street lighting networks, landline telephone networks and future wireless networks, and is universal in nature being independent of the operating frequency bands and traffic types. The main motivating factors for the proposed small cell concept are the need of public infrastructure re-engineering, and the recent advances in several enabling technologies. First, we highlight the main concepts of the proposed UnISCell platform. Subsequently, we present two deployment scenarios for the proposed UnISCell concept considering infrastructure sharing and service sharing as important aspects. We then describe the key future
technologies for enabling the proposed UnISCell concept and present a use case example with the help of numerical results. Finally, we conclude this article by providing some interesting future recommendations
Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks
Conventional cellular wireless networks were designed with the purpose of
providing high throughput for the user and high capacity for the service
provider, without any provisions of energy efficiency. As a result, these
networks have an enormous Carbon footprint. In this paper, we describe the
sources of the inefficiencies in such networks. First we present results of the
studies on how much Carbon footprint such networks generate. We also discuss
how much more mobile traffic is expected to increase so that this Carbon
footprint will even increase tremendously more. We then discuss specific
sources of inefficiency and potential sources of improvement at the physical
layer as well as at higher layers of the communication protocol hierarchy. In
particular, considering that most of the energy inefficiency in cellular
wireless networks is at the base stations, we discuss multi-tier networks and
point to the potential of exploiting mobility patterns in order to use base
station energy judiciously. We then investigate potential methods to reduce
this inefficiency and quantify their individual contributions. By a
consideration of the combination of all potential gains, we conclude that an
improvement in energy consumption in cellular wireless networks by two orders
of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843
Opportunistic Third-Party Backhaul for Cellular Wireless Networks
With high capacity air interfaces and large numbers of small cells, backhaul
-- the wired connectivity to base stations -- is increasingly becoming the cost
driver in cellular wireless networks. One reason for the high cost of backhaul
is that capacity is often purchased on leased lines with guaranteed rates
provisioned to peak loads. In this paper, we present an alternate
\emph{opportunistic backhaul} model where third parties provide base stations
and backhaul connections and lease out excess capacity in their networks to the
cellular provider when available, presumably at significantly lower costs than
guaranteed connections. We describe a scalable architecture for such
deployments using open access femtocells, which are small plug-and-play base
stations that operate in the carrier's spectrum but can connect directly into
the third party provider's wired network. Within the proposed architecture, we
present a general user association optimization algorithm that enables the
cellular provider to dynamically determine which mobiles should be assigned to
the third-party femtocells based on the traffic demands, interference and
channel conditions and third-party access pricing. Although the optimization is
non-convex, the algorithm uses a computationally efficient method for finding
approximate solutions via dual decomposition. Simulations of the deployment
model based on actual base station locations are presented that show that large
capacity gains are achievable if adoption of third-party, open access
femtocells can reach even a small fraction of the current market penetration of
WiFi access points.Comment: 9 pages, 6 figure
Compressive Sensing for Multi-channel and Large-scale MIMO Networks
Compressive sensing (CS) is a revolutionary theory that has important applications in many engineering areas. Using CS, sparse or compressible signals can be recovered from incoherent measurements with far fewer samples than the conventional Nyquist rate. In wireless communication problems where the sparsity structure of the signals and the channels can be explored and utilized, CS helps to significantly reduce the number of transmissions required to have an efficient and reliable data communication. The objective of this thesis is to study new methods of CS, both from theoretical and application perspectives, in various complex, multi-channel and large-scale wireless networks. Specifically, we explore new sparse signal and channel structures, and develop low-complexity CS-based algorithms to transmit and recover data over these networks more efficiently.
Starting from the theory of sparse vector approximation based on CS, a compressive multiple-channel estimation (CMCE) method is developed to estimate multiple sparse channels simultaneously. CMCE provides a reduction in the required overhead for the estimation of multiple channels, and can be applied to estimate the composite channels of two-way relay channels (TWRCs) with sparse intersymbol interference (ISI). To improve end-to-end error performance of the networks, various iterative estimation and decoding schemes based on CS for ISI-TWRC are proposed, for both modes of cooperative relaying: Amplify-and-Forward (AF) and Decode-and-Forward (DF). Theoretical results including the Restricted Isometry Property (RIP) and low-coherent condition of the discrete pilot signaling matrix, the performance guarantees, and the convergence of the schemes are presented in this thesis. Numerical results suggest that the error performances of the system is significantly improved by the proposed CS-based methods, thanks to the awareness of the sparsity feature of the channels.
Low-rank matrix approximation, an extension of CS-based sparse vector recovery theory, is then studied in this research to address the channel estimation problem of large-scale (or massive) multiuser (MU) multiple-input multiple-output (MIMO) systems. A low-rank channel matrix estimation method based on nuclear-norm regularization is formulated and solved via a dual quadratic semi-definite programming (SDP) problem. An explicit choice of the regularization parameter and useful upper bounds of the error are presented to show the efficacy of the CS method in this case. After that, both the uplink channel estimation and a downlink data recoding of massive MIMO in the interference-limited multicell scenarios are considered, where a CS-based rank-q channel approximation and multicell precoding method are proposed. The results in this work suggest that the proposed method can mitigate the effects of the pilot contamination and intercell interference, hence improves the achievable rates of the users in multicell massive MIMO systems. Finally, various low-complexity greedy techniques are then presented to confirm the efficacy and feasibility of the proposed approaches in practical applications
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