248 research outputs found
Resource allocation and optimization techniques in wireless relay networks
Relay techniques have the potential to enhance capacity and coverage of a wireless network. Due to rapidly increasing number of smart phone subscribers and high demand for data intensive multimedia applications, the
useful radio spectrum is becoming a scarce resource. For this reason, two way relay network and cognitive radio technologies are required for better utilization of radio spectrum. Compared to the conventional one way relay
network, both the uplink and the downlink can be served simultaneously using a two way relay network. Hence the effective bandwidth efficiency is considered to be one time slot per transmission. Cognitive networks are wireless networks that consist of different types of users, a primary user (PU, the primary license holder of a spectrum band) and secondary users (SU, cognitive radios that opportunistically access the PU spectrum). The
secondary users can access the spectrum of the licensed user provided they do not harmfully affect to the primary user. In this thesis, various resource
allocation and optimization techniques have been investigated for wireless relay and cognitive radio networks
Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems with Low-Resolution Data Converters
A multicell-coordinated beamforming solution for massive multiple-input
multiple-output orthogonal frequency-division multiplexing (OFDM) systems is
presented when employing low-resolution data converters and per-antenna level
constraints. For a more realistic deployment, we aim to find the downlink (DL)
beamformer that minimizes the maximum power on transmit antenna array of each
basestation under received signal quality constraints while minimizing
per-antenna transmit power. We show that strong duality holds between the
primal DL formulation and its manageable Lagrangian dual problem which can be
interpreted as the virtual uplink (UL) problem with adjustable noise covariance
matrices. For a fixed set of noise covariance matrices, we claim that the
virtual UL solution is effectively used to compute the DL beamformer and noise
covariance matrices can be subsequently updated with an associated subgradient.
Our primary contributions are then (1) formulating the quantized DL OFDM
antenna power minimax problem and deriving its associated dual problem, (2)
showing strong duality and interpreting the dual as a virtual quantized UL OFDM
problem, and (3) developing an iterative minimax algorithm based on the dual
problem. Simulations validate the proposed algorithm in terms of the maximum
antenna transmit power and peak-to-average-power ratio.Comment: submitted for possible IEEE journal publicatio
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
Multiuser MISO Transmitter Optimization for Inter-Cell Interference Mitigation
The transmitter optimization (i.e., steering vectors and power allocation)
for a MISO Broadcast Channel (MISO-BC) subject to general linear constraints is
considered. Such constraints include, as special cases, the sum power, the
per-antenna or per-group-of-antennas power, and "forbidden interference
direction" constraints. We consider both the optimal dirty-paper coding and the
simple suboptimal linear zero-forcing beamforming strategies, and provide
numerically efficient algorithms that solve the problem in its most general
form. As an application, we consider a multi-cell scenario with partial cell
cooperation, where each cell optimizes its precoder by taking into account
interference constraints on specific users in adjacent cells. The effectiveness
of the proposed methods is evaluated in a simple system scenario including two
adjacent cells, under different fairness criteria that emphasize the bottleneck
role of users near the cell "boundary". Our results show that "active"
Inter-Cell Interference (ICI) mitigation outperforms the conventional "static"
ICI mitigation based on fractional frequency reuse.Comment: 30 pages, 10 figures, and 1 table. revised and resubmitted to IEEE
Transactions on Signal Processin
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