55 research outputs found
High Throughput VLSI Architecture for Soft-Output MIMO Detection Based on A Greedy Graph Algorithm
Maximum-likelihood (ML) decoding is a very computational-
intensive task for multiple-input multiple-output (MIMO)
wireless channel detection. This paper presents a new graph
based algorithm to achieve near ML performance for soft
MIMO detection. Instead of using the traditional tree search
based structure, we represent the search space of the MIMO
signals with a directed graph and a greedy algorithm is ap-
plied to compute the a posteriori probability (APP) for each
transmitted bit. The proposed detector has two advantages:
1) it keeps a fixed throughput and has a regular and parallel
datapath structure which makes it amenable to high speed
VLSI implementation, and 2) it attempts to maximize the a
posteriori probability by making the locally optimum choice
at each stage with the hope of finding the global minimum
Euclidean distance for every transmitted bit x_k element of {-1, +1}.
Compared to the soft K-best detector, the proposed solution
significantly reduces the complexity because sorting is not
required, while still maintaining good bit error rate (BER)
performance. The proposed greedy detection algorithm has
been designed and synthesized for a 4 x 4 16-QAM MIMO
system in a TSMC 65 nm CMOS technology. The detector
achieves a maximum throughput of 600 Mbps with a 0.79
mm2 core area.Nokia CorporationNational Science Foundatio
On Finding a Subset of Healthy Individuals from a Large Population
In this paper, we derive mutual information based upper and lower bounds on
the number of nonadaptive group tests required to identify a given number of
"non defective" items from a large population containing a small number of
"defective" items. We show that a reduction in the number of tests is
achievable compared to the approach of first identifying all the defective
items and then picking the required number of non-defective items from the
complement set. In the asymptotic regime with the population size , to identify non-defective items out of a population
containing defective items, when the tests are reliable, our results show
that measurements are
sufficient, where is a constant independent of and , and
is a bounded function of and . Further, in the nonadaptive group
testing setup, we obtain rigorous upper and lower bounds on the number of tests
under both dilution and additive noise models. Our results are derived using a
general sparse signal model, by virtue of which, they are also applicable to
other important sparse signal based applications such as compressive sensing.Comment: 32 pages, 2 figures, 3 tables, revised version of a paper submitted
to IEEE Trans. Inf. Theor
Computationally Tractable Algorithms for Finding a Subset of Non-defective Items from a Large Population
In the classical non-adaptive group testing setup, pools of items are tested
together, and the main goal of a recovery algorithm is to identify the
"complete defective set" given the outcomes of different group tests. In
contrast, the main goal of a "non-defective subset recovery" algorithm is to
identify a "subset" of non-defective items given the test outcomes. In this
paper, we present a suite of computationally efficient and analytically
tractable non-defective subset recovery algorithms. By analyzing the
probability of error of the algorithms, we obtain bounds on the number of tests
required for non-defective subset recovery with arbitrarily small probability
of error. Our analysis accounts for the impact of both the additive noise
(false positives) and dilution noise (false negatives). By comparing with the
information theoretic lower bounds, we show that the upper bounds on the number
of tests are order-wise tight up to a factor, where is the number
of defective items. We also provide simulation results that compare the
relative performance of the different algorithms and provide further insights
into their practical utility. The proposed algorithms significantly outperform
the straightforward approaches of testing items one-by-one, and of first
identifying the defective set and then choosing the non-defective items from
the complement set, in terms of the number of measurements required to ensure a
given success rate.Comment: In this revision: Unified some proofs and reorganized the paper,
corrected a small mistake in one of the proofs, added more reference
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