2,143 research outputs found
A Random Search Framework for Convergence Analysis of Distributed Beamforming with Feedback
The focus of this work is on the analysis of transmit beamforming schemes
with a low-rate feedback link in wireless sensor/relay networks, where nodes in
the network need to implement beamforming in a distributed manner.
Specifically, the problem of distributed phase alignment is considered, where
neither the transmitters nor the receiver has perfect channel state
information, but there is a low-rate feedback link from the receiver to the
transmitters. In this setting, a framework is proposed for systematically
analyzing the performance of distributed beamforming schemes. To illustrate the
advantage of this framework, a simple adaptive distributed beamforming scheme
that was recently proposed by Mudambai et al. is studied. Two important
properties for the received signal magnitude function are derived. Using these
properties and the systematic framework, it is shown that the adaptive
distributed beamforming scheme converges both in probability and in mean.
Furthermore, it is established that the time required for the adaptive scheme
to converge in mean scales linearly with respect to the number of sensor/relay
nodes.Comment: 8 pages, 3 figures, presented partially at ITA '08 and PSU School of
Info. Theory '0
Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness
This paper addresses coordinated downlink beamforming optimization in
multicell time-division duplex (TDD) systems where a small number of parameters
are exchanged between cells but with no data sharing. With the goal to reach
the point on the Pareto boundary with max-min rate fairness, we first develop a
two-step centralized optimization algorithm to design the joint beamforming
vectors. This algorithm can achieve a further sum-rate improvement over the
max-min optimal performance, and is shown to guarantee max-min Pareto
optimality for scenarios with two base stations (BSs) each serving a single
user. To realize a distributed solution with limited intercell communication,
we then propose an iterative algorithm by exploiting an approximate
uplink-downlink duality, in which only a small number of positive scalars are
shared between cells in each iteration. Simulation results show that the
proposed distributed solution achieves a fairness rate performance close to the
centralized algorithm while it has a better sum-rate performance, and
demonstrates a better tradeoff between sum-rate and fairness than the Nash
Bargaining solution especially at high signal-to-noise ratio.Comment: 8 figures. To Appear in IEEE Trans. Wireless Communications, 201
Coordination and Antenna Domain Formation in Cloud-RAN systems
We study here the problem of Antenna Domain Formation (ADF) in cloud RAN
systems, whereby multiple remote radio-heads (RRHs) are each to be assigned to
a set of antenna domains (ADs), such that the total interference between the
ADs is minimized. We formulate the corresponding optimization problem, by
introducing the concept of \emph{interference coupling coefficients} among
pairs of radio-heads. We then propose a low-overhead algorithm that allows the
problem to be solved in a distributed fashion, among the aggregation nodes
(ANs), and establish basic convergence results. Moreover, we also propose a
simple relaxation to the problem, thus enabling us to characterize its maximum
performance. We follow a layered coordination structure: after the ADs are
formed, radio-heads are clustered to perform coordinated beamforming using the
well known Weighted-MMSE algorithm. Finally, our simulations show that using
the proposed ADF mechanism would significantly increase the sum-rate of the
system (with respect to random assignment of radio-heads).Comment: 7 pages, IEEE International Conference on Communications 2016 (ICC
2016
Outage Capacity of Opportunistic Beamforming with Random User Locations
This paper studies the outage capacity of a network consisting of a multitude
of heterogenous mobile users, and operating according to the classical
opportunistic beamforming framework. The base station is located at the center
of the cell, which is modeled as a disk of finite radius. The random user
locations are modeled using a homogenous spatial Poisson point process. The
received signals are impaired by both fading and location dependent path loss.
For this system, we first derive an expression for the beam outage probability.
This expression holds for all path loss models that satisfy some mild
conditions. Then, we focus on two specific path loss models (i.e., an unbounded
model and a more realistic bounded one) to illustrate the applications of our
results. In the large system limit where the cell radius tends to infinity, the
beam outage capacity and its scaling behavior are derived for the selected
specific path loss models. It is shown that the beam outage capacity scales
logarithmically for the unbounded model. On the other hand, this scaling
behavior becomes double logarithmic for the bounded model. Intuitive
explanations are provided as to why we observe different scaling behavior for
different path loss models. Numerical evaluations are performed to give further
insights, and to illustrate the applicability of the outage capacity results
even to a cell having a small finite radius.Comment: To appear in Globecom 2013, Atlanta, US
Optimality Properties, Distributed Strategies, and Measurement-Based Evaluation of Coordinated Multicell OFDMA Transmission
The throughput of multicell systems is inherently limited by interference and
the available communication resources. Coordinated resource allocation is the
key to efficient performance, but the demand on backhaul signaling and
computational resources grows rapidly with number of cells, terminals, and
subcarriers. To handle this, we propose a novel multicell framework with
dynamic cooperation clusters where each terminal is jointly served by a small
set of base stations. Each base station coordinates interference to neighboring
terminals only, thus limiting backhaul signalling and making the framework
scalable. This framework can describe anything from interference channels to
ideal joint multicell transmission.
The resource allocation (i.e., precoding and scheduling) is formulated as an
optimization problem (P1) with performance described by arbitrary monotonic
functions of the signal-to-interference-and-noise ratios (SINRs) and arbitrary
linear power constraints. Although (P1) is non-convex and difficult to solve
optimally, we are able to prove: 1) Optimality of single-stream beamforming; 2)
Conditions for full power usage; and 3) A precoding parametrization based on a
few parameters between zero and one. These optimality properties are used to
propose low-complexity strategies: both a centralized scheme and a distributed
version that only requires local channel knowledge and processing. We evaluate
the performance on measured multicell channels and observe that the proposed
strategies achieve close-to-optimal performance among centralized and
distributed solutions, respectively. In addition, we show that multicell
interference coordination can give substantial improvements in sum performance,
but that joint transmission is very sensitive to synchronization errors and
that some terminals can experience performance degradations.Comment: Published in IEEE Transactions on Signal Processing, 15 pages, 7
figures. This version corrects typos related to Eq. (4) and Eq. (28
Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks
In this paper, we develop various beamforming techniques for downlink
transmission for multiple-input single-output (MISO) non-orthogonal multiple
access (NOMA) systems. First, a beamforming approach with perfect channel state
information (CSI) is investigated to provide the required quality of service
(QoS) for all users. Taylor series approximation and semidefinite relaxation
(SDR) techniques are employed to reformulate the original non-convex power
minimization problem to a tractable one. Further, a fairness-based beamforming
approach is proposed through a max-min formulation to maintain fairness between
users. Next, we consider a robust scheme by incorporating channel
uncertainties, where the transmit power is minimized while satisfying the
outage probability requirement at each user. Through exploiting the SDR
approach, the original non-convex problem is reformulated in a linear matrix
inequality (LMI) form to obtain the optimal solution. Numerical results
demonstrate that the robust scheme can achieve better performance compared to
the non-robust scheme in terms of the rate satisfaction ratio. Further,
simulation results confirm that NOMA consumes a little over half transmit power
needed by OMA for the same data rate requirements. Hence, NOMA has the
potential to significantly improve the system performance in terms of transmit
power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog
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