784 research outputs found
Partial coordination in clustered base station MIMO transmission
This proceeding at: IEEE Wireless Communications and Networking Conference (WCNC, 2013), took place 2013, April, 7-10, in Shaghai (China)We present partial coordination strategies in a clustered cellular environment, evaluating the achievable rate in the downlink transmission. Block Diagonalization is employed for the coordinated users within the cluster to remove interference, while the interference from non-coordinated users remains. The achievable rate is evaluated resorting to an analytical expression conditioned on the position of the users in the cluster. A partial coordination approach is proposed to reduce the coordination complexity and overhead, where users close to the base station are not coordinated. Two approaches are considered, namely the non-coordinated users can be grouped and assigned separated resources from the coordinated ones, or they can be mixed.This work was supported by projects CSD2008-00010
“COMONSENS” and TEC2011-29006-C03-03 “GRE3N”
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
Network MIMO with Partial Cooperation between Radar and Cellular Systems
To meet the growing spectrum demands, future cellular systems are expected to
share the spectrum of other services such as radar. In this paper, we consider
a network multiple-input multiple-output (MIMO) with partial cooperation model
where radar stations cooperate with cellular base stations (BS)s to deliver
messages to intended mobile users. So the radar stations act as BSs in the
cellular system. However, due to the high power transmitted by radar stations
for detection of far targets, the cellular receivers could burnout when
receiving these high radar powers. Therefore, we propose a new projection
method called small singular values space projection (SSVSP) to mitigate these
harmful high power and enable radar stations to collaborate with cellular base
stations. In addition, we formulate the problem into a MIMO interference
channel with general constraints (MIMO-IFC-GC). Finally, we provide a solution
to minimize the weighted sum mean square error minimization problem (WSMMSE)
with enforcing power constraints on both radar and cellular stations.Comment: (c) 2015 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
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redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
Linear Precoding and Equalization for Network MIMO with Partial Cooperation
A cellular multiple-input multiple-output (MIMO) downlink system is studied
in which each base station (BS) transmits to some of the users, so that each
user receives its intended signal from a subset of the BSs. This scenario is
referred to as network MIMO with partial cooperation, since only a subset of
the BSs are able to coordinate their transmission towards any user. The focus
of this paper is on the optimization of linear beamforming strategies at the
BSs and at the users for network MIMO with partial cooperation. Individual
power constraints at the BSs are enforced, along with constraints on the number
of streams per user. It is first shown that the system is equivalent to a MIMO
interference channel with generalized linear constraints (MIMO-IFC-GC). The
problems of maximizing the sum-rate(SR) and minimizing the weighted sum mean
square error (WSMSE) of the data estimates are non-convex, and suboptimal
solutions with reasonable complexity need to be devised. Based on this,
suboptimal techniques that aim at maximizing the sum-rate for the MIMO-IFC-GC
are reviewed from recent literature and extended to the MIMO-IFC-GC where
necessary. Novel designs that aim at minimizing the WSMSE are then proposed.
Extensive numerical simulations are provided to compare the performance of the
considered schemes for realistic cellular systems.Comment: 13 pages, 5 figures, published in IEEE Transactions on Vehicular
Technology, June 201
Opportunistic Scheduling for Full-Duplex Uplink-Downlink Networks
We study opportunistic scheduling and the sum capacity of cellular networks
with a full-duplex multi-antenna base station and a large number of
single-antenna half-duplex users. Simultaneous uplink and downlink over the
same band results in uplink-to-downlink interference, degrading performance. We
present a simple opportunistic joint uplink-downlink scheduling algorithm that
exploits multiuser diversity and treats interference as noise. We show that in
homogeneous networks, our algorithm achieves the same sum capacity as what
would have been achieved if there was no uplink-to-downlink interference,
asymptotically in the number of users. The algorithm does not require
interference CSI at the base station or uplink users. It is also shown that for
a simple class of heterogeneous networks without sufficient channel diversity,
it is not possible to achieve the corresponding interference-free system
capacity. We discuss the potential for using device-to-device side-channels to
overcome this limitation in heterogeneous networks.Comment: 10 pages, 2 figures, to appear at IEEE International Symposium on
Information Theory (ISIT) '1
Mean Achievable Rates in Clustered Coordinated Base Station Transmission with Block Diagonalization
We focus on the mean achievable rate per user of the coordinated base station downlink transmission in a clustered cellular environment, with transmit power constraints at the base stations. Block Diagonalization is employed within the cluster to remove interference among users while the interference from other clusters remains. The average achievable rate per user is evaluated considering the effects of the propagation channel and the interference and a theoretical framework is presented to provide its analytical expression, validated by simulation results with different power allocation schemes. As an application, the number of cells of the cluster that maximizes the mean achievable rate per user is investigated. It can be seen that in most of the cases a reduced cluster size, close to seven cells, guarantees a rate very close to the maximum achievableThis work is partly funded by projects "GRE3N": TEC2011-29006-C03-03
and "COMONSENS": CSD2008-00010En-prens
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