9,796 research outputs found
Full Diversity Space-Time Block Codes with Low-Complexity Partial Interference Cancellation Group Decoding
Partial interference cancellation (PIC) group decoding proposed by Guo and
Xia is an attractive low-complexity alternative to the optimal processing for
multiple-input multiple-output (MIMO) wireless communications. It can well deal
with the tradeoff among rate, diversity and complexity of space-time block
codes (STBC). In this paper, a systematic design of full-diversity STBC with
low-complexity PIC group decoding is proposed. The proposed code design is
featured as a group-orthogonal STBC by replacing every element of an Alamouti
code matrix with an elementary matrix composed of multiple diagonal layers of
coded symbols. With the PIC group decoding and a particular grouping scheme,
the proposed STBC can achieve full diversity, a rate of and a
low-complexity decoding for transmit antennas. Simulation results show that
the proposed codes can achieve the full diversity with PIC group decoding while
requiring half decoding complexity of the existing codes.Comment: 10 pages, 3 figures
MMSE Optimal Algebraic Space-Time Codes
Design of Space-Time Block Codes (STBCs) for Maximum Likelihood (ML)
reception has been predominantly the main focus of researchers. However, the ML
decoding complexity of STBCs becomes prohibitive large as the number of
transmit and receive antennas increase. Hence it is natural to resort to a
suboptimal reception technique like linear Minimum Mean Squared Error (MMSE)
receiver. Barbarossa et al and Liu et al have independently derived necessary
and sufficient conditions for a full rate linear STBC to be MMSE optimal, i.e
achieve least Symbol Error Rate (SER). Motivated by this problem, certain
existing high rate STBC constructions from crossed product algebras are
identified to be MMSE optimal. Also, it is shown that a certain class of codes
from cyclic division algebras which are special cases of crossed product
algebras are MMSE optimal. Hence, these STBCs achieve least SER when MMSE
reception is employed and are fully diverse when ML reception is employed.Comment: 5 pages, 1 figure, journal version to appear in IEEE Transactions on
Wireless Communications. Conference version appeared in NCC 2007, IIT Kanpur,
Indi
DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models
The work identifies the first general, explicit, and non-random MIMO
encoder-decoder structures that guarantee optimality with respect to the
diversity-multiplexing tradeoff (DMT), without employing a computationally
expensive maximum-likelihood (ML) receiver. Specifically, the work establishes
the DMT optimality of a class of regularized lattice decoders, and more
importantly the DMT optimality of their lattice-reduction (LR)-aided linear
counterparts. The results hold for all channel statistics, for all channel
dimensions, and most interestingly, irrespective of the particular lattice-code
applied. As a special case, it is established that the LLL-based LR-aided
linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal
decoding of any lattice code at a worst-case complexity that grows at most
linearly in the data rate. This represents a fundamental reduction in the
decoding complexity when compared to ML decoding whose complexity is generally
exponential in rate.
The results' generality lends them applicable to a plethora of pertinent
communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI,
cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality
of the LR-aided linear decoder is guaranteed. The adopted approach yields
insight, and motivates further study, into joint transceiver designs with an
improved SNR gap to ML decoding.Comment: 16 pages, 1 figure (3 subfigures), submitted to the IEEE Transactions
on Information Theor
Integer-Forcing Linear Receivers
Linear receivers are often used to reduce the implementation complexity of
multiple-antenna systems. In a traditional linear receiver architecture, the
receive antennas are used to separate out the codewords sent by each transmit
antenna, which can then be decoded individually. Although easy to implement,
this approach can be highly suboptimal when the channel matrix is near
singular. This paper develops a new linear receiver architecture that uses the
receive antennas to create an effective channel matrix with integer-valued
entries. Rather than attempting to recover transmitted codewords directly, the
decoder recovers integer combinations of the codewords according to the entries
of the effective channel matrix. The codewords are all generated using the same
linear code which guarantees that these integer combinations are themselves
codewords. Provided that the effective channel is full rank, these integer
combinations can then be digitally solved for the original codewords. This
paper focuses on the special case where there is no coding across transmit
antennas and no channel state information at the transmitter(s), which
corresponds either to a multi-user uplink scenario or to single-user V-BLAST
encoding. In this setting, the proposed integer-forcing linear receiver
significantly outperforms conventional linear architectures such as the
zero-forcing and linear MMSE receiver. In the high SNR regime, the proposed
receiver attains the optimal diversity-multiplexing tradeoff for the standard
MIMO channel with no coding across transmit antennas. It is further shown that
in an extended MIMO model with interference, the integer-forcing linear
receiver achieves the optimal generalized degrees-of-freedom.Comment: 40 pages, 16 figures, to appear in the IEEE Transactions on
Information Theor
Precoded Integer-Forcing Universally Achieves the MIMO Capacity to Within a Constant Gap
An open-loop single-user multiple-input multiple-output communication scheme
is considered where a transmitter, equipped with multiple antennas, encodes the
data into independent streams all taken from the same linear code. The coded
streams are then linearly precoded using the encoding matrix of a perfect
linear dispersion space-time code. At the receiver side, integer-forcing
equalization is applied, followed by standard single-stream decoding. It is
shown that this communication architecture achieves the capacity of any
Gaussian multiple-input multiple-output channel up to a gap that depends only
on the number of transmit antennas.Comment: to appear in the IEEE Transactions on Information Theor
Open-Loop Spatial Multiplexing and Diversity Communications in Ad Hoc Networks
This paper investigates the performance of open-loop multi-antenna
point-to-point links in ad hoc networks with slotted ALOHA medium access
control (MAC). We consider spatial multiplexing transmission with linear
maximum ratio combining and zero forcing receivers, as well as orthogonal space
time block coded transmission. New closed-form expressions are derived for the
outage probability, throughput and transmission capacity. Our results
demonstrate that both the best performing scheme and the optimum number of
transmit antennas depend on different network parameters, such as the node
intensity and the signal-to-interference-and-noise ratio operating value. We
then compare the performance to a network consisting of single-antenna devices
and an idealized fully centrally coordinated MAC. These results show that
multi-antenna schemes with a simple decentralized slotted ALOHA MAC can
outperform even idealized single-antenna networks in various practical
scenarios.Comment: 51 pages, 19 figures, submitted to IEEE Transactions on Information
Theor
Cooperative Compute-and-Forward
We examine the benefits of user cooperation under compute-and-forward. Much
like in network coding, receivers in a compute-and-forward network recover
finite-field linear combinations of transmitters' messages. Recovery is enabled
by linear codes: transmitters map messages to a linear codebook, and receivers
attempt to decode the incoming superposition of signals to an integer
combination of codewords. However, the achievable computation rates are low if
channel gains do not correspond to a suitable linear combination. In response
to this challenge, we propose a cooperative approach to compute-and-forward. We
devise a lattice-coding approach to block Markov encoding with which we
construct a decode-and-forward style computation strategy. Transmitters
broadcast lattice codewords, decode each other's messages, and then
cooperatively transmit resolution information to aid receivers in decoding the
integer combinations. Using our strategy, we show that cooperation offers a
significant improvement both in the achievable computation rate and in the
diversity-multiplexing tradeoff.Comment: submitted to IEEE Transactions on Information Theor
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