2,231,511 research outputs found
High-Rate Space-Time Coded Large MIMO Systems: Low-Complexity Detection and Channel Estimation
In this paper, we present a low-complexity algorithm for detection in
high-rate, non-orthogonal space-time block coded (STBC) large-MIMO systems that
achieve high spectral efficiencies of the order of tens of bps/Hz. We also
present a training-based iterative detection/channel estimation scheme for such
large STBC MIMO systems. Our simulation results show that excellent bit error
rate and nearness-to-capacity performance are achieved by the proposed
multistage likelihood ascent search (M-LAS) detector in conjunction with the
proposed iterative detection/channel estimation scheme at low complexities. The
fact that we could show such good results for large STBCs like 16x16 and 32x32
STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in
excess of 20 bps/Hz (even after accounting for the overheads meant for pilot
based training for channel estimation and turbo coding) establishes the
effectiveness of the proposed detector and channel estimator. We decode perfect
codes of large dimensions using the proposed detector. With the feasibility of
such a low-complexity detection/channel estimation scheme, large-MIMO systems
with tens of antennas operating at several tens of bps/Hz spectral efficiencies
can become practical, enabling interesting high data rate wireless
applications.Comment: v3: Performance/complexity comparison of the proposed scheme with
other large-MIMO architectures/detectors has been added (Sec. IV-D). The
paper has been accepted for publication in IEEE Journal of Selected Topics in
Signal Processing (JSTSP): Spl. Iss. on Managing Complexity in Multiuser MIMO
Systems. v2: Section V on Channel Estimation is update
Holographic non-computers
We introduce the notion of holographic non-computer as a system which
exhibits parametrically large delays in the growth of complexity, as calculated
within the Complexity-Action proposal. Some known examples of this behavior
include extremal black holes and near-extremal hyperbolic black holes. Generic
black holes in higher-dimensional gravity also show non-computing features.
Within the expansion of General Relativity, we show that large-
scalings which capture the qualitative features of complexity, such as a linear
growth regime and a plateau at exponentially long times, also exhibit an
initial computational delay proportional to . While consistent for large AdS
black holes, the required `non-computing' scalings are incompatible with
thermodynamic stability for Schwarzschild black holes, unless they are tightly
caged.Comment: 23 pages, 7 figures. V3: References added. Figures updated. New
discussion of small black holes in the canonical ensembl
Coarse-graining of cellular automata, emergence, and the predictability of complex systems
We study the predictability of emergent phenomena in complex systems. Using
nearest neighbor, one-dimensional Cellular Automata (CA) as an example, we show
how to construct local coarse-grained descriptions of CA in all classes of
Wolfram's classification. The resulting coarse-grained CA that we construct are
capable of emulating the large-scale behavior of the original systems without
accounting for small-scale details. Several CA that can be coarse-grained by
this construction are known to be universal Turing machines; they can emulate
any CA or other computing devices and are therefore undecidable. We thus show
that because in practice one only seeks coarse-grained information, complex
physical systems can be predictable and even decidable at some level of
description. The renormalization group flows that we construct induce a
hierarchy of CA rules. This hierarchy agrees well with apparent rule complexity
and is therefore a good candidate for a complexity measure and a classification
method. Finally we argue that the large scale dynamics of CA can be very
simple, at least when measured by the Kolmogorov complexity of the large scale
update rule, and moreover exhibits a novel scaling law. We show that because of
this large-scale simplicity, the probability of finding a coarse-grained
description of CA approaches unity as one goes to increasingly coarser scales.
We interpret this large scale simplicity as a pattern formation mechanism in
which large scale patterns are forced upon the system by the simplicity of the
rules that govern the large scale dynamics.Comment: 18 pages, 9 figure
Statistical Pruning for Near-Maximum Likelihood Decoding
In many communications problems, maximum-likelihood (ML) decoding reduces to finding the closest (skewed) lattice point in N-dimensions to a given point xisin CN. In its full generality, this problem is known to be NP-complete. Recently, the expected complexity of the sphere decoder, a particular algorithm that solves the ML problem exactly, has been computed. An asymptotic analysis of this complexity has also been done where it is shown that the required computations grow exponentially in N for any fixed SNR. At the same time, numerical computations of the expected complexity show that there are certain ranges of rates, SNRs and dimensions N for which the expected computation (counted as the number of scalar multiplications) involves no more than N3 computations. However, when the dimension of the problem grows too large, the required computations become prohibitively large, as expected from the asymptotic exponential complexity. In this paper, we propose an algorithm that, for large N, offers substantial computational savings over the sphere decoder, while maintaining performance arbitrarily close to ML. We statistically prune the search space to a subset that, with high probability, contains the optimal solution, thereby reducing the complexity of the search. Bounds on the error performance of the new method are proposed. The complexity of the new algorithm is analyzed through an upper bound. The asymptotic behavior of the upper bound for large N is also analyzed which shows that the upper bound is also exponential but much lower than the sphere decoder. Simulation results show that the algorithm is much more efficient than the original sphere decoder for smaller dimensions as well, and does not sacrifice much in terms of performance
- …