3,677 research outputs found
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
Sparsity Enhanced Decision Feedback Equalization
For single-carrier systems with frequency domain equalization, decision
feedback equalization (DFE) performs better than linear equalization and has
much lower computational complexity than sequence maximum likelihood detection.
The main challenge in DFE is the feedback symbol selection rule. In this paper,
we give a theoretical framework for a simple, sparsity based thresholding
algorithm. We feed back multiple symbols in each iteration, so the algorithm
converges fast and has a low computational cost. We show how the initial
solution can be obtained via convex relaxation instead of linear equalization,
and illustrate the impact that the choice of the initial solution has on the
bit error rate performance of our algorithm. The algorithm is applicable in
several existing wireless communication systems (SC-FDMA, MC-CDMA, MIMO-OFDM).
Numerical results illustrate significant performance improvement in terms of
bit error rate compared to the MMSE solution
Error Rates of the Maximum-Likelihood Detector for Arbitrary Constellations: Convex/Concave Behavior and Applications
Motivated by a recent surge of interest in convex optimization techniques,
convexity/concavity properties of error rates of the maximum likelihood
detector operating in the AWGN channel are studied and extended to
frequency-flat slow-fading channels. Generic conditions are identified under
which the symbol error rate (SER) is convex/concave for arbitrary
multi-dimensional constellations. In particular, the SER is convex in SNR for
any one- and two-dimensional constellation, and also in higher dimensions at
high SNR. Pairwise error probability and bit error rate are shown to be convex
at high SNR, for arbitrary constellations and bit mapping. Universal bounds for
the SER 1st and 2nd derivatives are obtained, which hold for arbitrary
constellations and are tight for some of them. Applications of the results are
discussed, which include optimum power allocation in spatial multiplexing
systems, optimum power/time sharing to decrease or increase (jamming problem)
error rate, an implication for fading channels ("fading is never good in low
dimensions") and optimization of a unitary-precoded OFDM system. For example,
the error rate bounds of a unitary-precoded OFDM system with QPSK modulation,
which reveal the best and worst precoding, are extended to arbitrary
constellations, which may also include coding. The reported results also apply
to the interference channel under Gaussian approximation, to the bit error rate
when it can be expressed or approximated as a non-negative linear combination
of individual symbol error rates, and to coded systems.Comment: accepted by IEEE IT Transaction
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