136 research outputs found
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
A novel approach to MISO interference networks under maximum receive-power regulation
An aggressive frequency reuse is expected within the next years in order to increase the spectral ef¿ciency. Multiuser interference by all in-band transmitters can create a communication bottleneck and, therefore, it is compulsory to control it by means of radiated power regulations. In this work we consider received power as the main way to properly measure radiated power, serving at the same time as a spectrum sharing mechanism. Taking into account the constraints on the maximum total receive-power and maximum transmit-power, we ¿rst obtain the transmit powers that attain the Pareto-ef¿cient rates in an uncoordinated network. Among these rates, we identify the maximum sum-rate point for noise-limited scenarios. Next, in order to reach this working point using as less power as possible, we design a novel beamformer under some practical considerations. This beamformer can be calculated in a non-iterative and distributed fashion (i.e. transmitters do not need to exchange information). We evaluateour designby meansof Monte Carlosimulations, compare it with other non-iterative transmit beam formers and show its superior performance when the spectrum sharing receive-power constraints are imposed.Peer ReviewedPostprint (published version
Doctor of Philosophy
dissertationThis dissertation addresses several key challenges in multiple-antenna communications, including information-theoretical analysis of channel capacity, capacity-achieving signaling design, and practical statistical detection algorithms. The first part of the thesis studies the capacity limits of multiple-input multiple-output (MIMO) multiple access channel (MAC) via virtual representation (VR) model. The VR model captures the physical scattering environment via channel gains in the angular domain, and hence is a realistic MIMO channel model that includes many existing channel models as special cases. This study provides analytical characterization of the optimal input distribution that achieves the sum-capacity of MAC-VR. It also investigates the optimality of beamforming, which is a simple scalar coding strategy desirable in practice. For temporally correlated channels, beamforming codebook designs are proposed that can efficiently exploit channel correlation. The second part of the thesis focuses on statistical detection for time-varying frequency-selective channels. The proposed statistical detectors are developed based on Markov Chain Monte Carlo (MCMC) techniques. The complexity of such detectors grows linearly in system dimensions, which renders them applicable to inter-symbol-interference (ISI) channels with long delay spread, for which the traditional trellis-based detectors fail due to prohibitive complexity. The proposed MCMC detectors provide substantial gain over the de facto turbo minimum-mean square-error (MMSE) detector for both synthetic channel and underwater acoustic (UWA) channels. The effectiveness of the proposed MCMC detectors is successfully validated through experimental data collected from naval at-sea experiments
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
In downlink multi-antenna systems with many users, the multiplexing gain is
strictly limited by the number of transmit antennas and the use of these
antennas. Assuming that the total number of receive antennas at the
multi-antenna users is much larger than , the maximal multiplexing gain can
be achieved with many different transmission/reception strategies. For example,
the excess number of receive antennas can be utilized to schedule users with
effective channels that are near-orthogonal, for multi-stream multiplexing to
users with well-conditioned channels, and/or to enable interference-aware
receive combining. In this paper, we try to answer the question if the data
streams should be divided among few users (many streams per user) or many users
(few streams per user, enabling receive combining). Analytic results are
derived to show how user selection, spatial correlation, heterogeneous user
conditions, and imperfect channel acquisition (quantization or estimation
errors) affect the performance when sending the maximal number of streams or
one stream per scheduled user---the two extremes in data stream allocation.
While contradicting observations on this topic have been reported in prior
works, we show that selecting many users and allocating one stream per user
(i.e., exploiting receive combining) is the best candidate under realistic
conditions. This is explained by the provably stronger resilience towards
spatial correlation and the larger benefit from multi-user diversity. This
fundamental result has positive implications for the design of downlink systems
as it reduces the hardware requirements at the user devices and simplifies the
throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/one-or-multiple-stream
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