7,284 research outputs found
Reduced-Complexity Noncoherently Detected Differential Space-Time Shift Keying
Motivated by the recent development of Spatial Modulation (SM) and Differential Space-Time Shift Keying (DSTSK), we propose a reduced-complexity Conventional Differential Detector (CDD) as well as its reduced-complexity Multiple-Symbol Differential Sphere Detector (MSDSD) counterpart for DSTSK. Both schemes operate on a symbol-by-symbol basis in order to reduce the complexity of the classic block-by-block-based CDD and MSDSD, whilst still approaching the optimum performance of the full-search-based Maximum Likelihood (ML) detector. More explicitly, we carefully consider the objective function to be used for decoding the index of the specific antenna activated by taking into account the particular modulation scheme employed. Our simulation results demonstrate that the proposed CDD and MSDSD designed for DSTSK guarantee a significant complexity reduction compared to the classic block-based decoders, especially for high-rate DSTSK schemes, which is achieved without a performance penalty
Iterative decoding for MIMO channels via modified sphere decoding
In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain
Iterative Near-Maximum-Likelihood Detection in Rank-Deficient Downlink SDMA Systems
Abstract—In this paper, a precoded and iteratively detected downlink multiuser system is proposed, which is capable of operating in rankdeficient scenarios, when the number of transmitters exceeds the number of receivers. The literature of uplink space division multiple access (SDMA) systems is rich, but at the time of writing there is a paucity of information on the employment of SDMA techniques in the downlink. Hence, we propose a novel precoded downlink SDMA (DL-SDMA) multiuser communication system, which invokes a low-complexity nearmaximum-likelihood sphere decoder and is particularly suitable for the aforementioned rank-deficient scenario. Powerful iterative decoding is carried out by exchanging extrinsic information between the precoder’s decoder and the outer channel decoder. Furthermore, we demonstrate with the aid of extrinsic information transfer charts that our proposed precoded DL-SDMA system has a better convergence behavior than its nonprecoded DL-SDMA counterpart. Quantitatively, the proposed system having a normalized system load of Ls = 1.333, i.e., 1.333 times higher effective throughput facilitated by having 1.333 times more DL-SDMA transmitters than receivers, exhibits a “turbo cliff” at an Eb/N0 of 5 dB and hence results in an infinitesimally low bit error rate (BER). By contrast, at Eb/N0 = 5 dB, the equivalent system dispensing with precoding exhibits a BER in excess of 10%. Index Terms—Iterative decoding, maximum likelihood detection, space division multiple access (SDMA) downlink, sphere decoding
Reduced Complexity Sphere Decoding
In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can
achieve performance equivalent to full search Maximum Likelihood (ML) decoding,
with reduced complexity. Several researchers reported techniques that reduce
the complexity of SD further. In this paper, a new technique is introduced
which decreases the computational complexity of SD substantially, without
sacrificing performance. The reduction is accomplished by deconstructing the
decoding metric to decrease the number of computations and exploiting the
structure of a lattice representation. Furthermore, an application of SD,
employing a proposed smart implementation with very low computational
complexity is introduced. This application calculates the soft bit metrics of a
bit-interleaved convolutional-coded MIMO system in an efficient manner. Based
on the reduced complexity SD, the proposed smart implementation employs the
initial radius acquired by Zero-Forcing Decision Feedback Equalization (ZF-DFE)
which ensures no empty spheres. Other than that, a technique of a particular
data structure is also incorporated to efficiently reduce the number of
executions carried out by SD. Simulation results show that these approaches
achieve substantial gains in terms of the computational complexity for both
uncoded and coded MIMO systems.Comment: accepted to Journal. arXiv admin note: substantial text overlap with
arXiv:1009.351
Multiple Beamforming with Perfect Coding
Perfect Space-Time Block Codes (PSTBCs) achieve full diversity, full rate,
nonvanishing constant minimum determinant, uniform average transmitted energy
per antenna, and good shaping. However, the high decoding complexity is a
critical issue for practice. When the Channel State Information (CSI) is
available at both the transmitter and the receiver, Singular Value
Decomposition (SVD) is commonly applied for a Multiple-Input Multiple-Output
(MIMO) system to enhance the throughput or the performance. In this paper, two
novel techniques, Perfect Coded Multiple Beamforming (PCMB) and Bit-Interleaved
Coded Multiple Beamforming with Perfect Coding (BICMB-PC), are proposed,
employing both PSTBCs and SVD with and without channel coding, respectively.
With CSI at the transmitter (CSIT), the decoding complexity of PCMB is
substantially reduced compared to a MIMO system employing PSTBC, providing a
new prospect of CSIT. Especially, because of the special property of the
generation matrices, PCMB provides much lower decoding complexity than the
state-of-the-art SVD-based uncoded technique in dimensions 2 and 4. Similarly,
the decoding complexity of BICMB-PC is much lower than the state-of-the-art
SVD-based coded technique in these two dimensions, and the complexity gain is
greater than the uncoded case. Moreover, these aforementioned complexity
reductions are achieved with only negligible or modest loss in performance.Comment: accepted to journa
Bit-Interleaved Coded Multiple Beamforming with Perfect Coding
When the Channel State Information (CSI) is known by both the transmitter and
the receiver, beamforming techniques employing Singular Value Decomposition
(SVD) are commonly used in Multiple-Input Multiple-Output (MIMO) systems.
Without channel coding, there is a trade-off between full diversity and full
multiplexing. When channel coding is added, both of them can be achieved as
long as the code rate Rc and the number of employed subchannels S satisfy the
condition RcS<=1. By adding a properly designed constellation precoder, both
full diversity and full multiplexing can be achieved for both uncoded and coded
systems with the trade-off of a higher decoding complexity, e.g., Fully
Precoded Multiple Beamforming (FPMB) and Bit-Interleaved Coded Multiple
Beamforming with Full Precoding (BICMB-FP) without the condition RcS<=1.
Recently discovered Perfect Space-Time Block Code (PSTBC) is a full-rate
full-diversity space-time code, which achieves efficient shaping and high
coding gain for MIMO systems. In this paper, a new technique, Bit-Interleaved
Coded Multiple Beamforming with Perfect Coding (BICMB-PC), is introduced.
BICMB-PC transmits PSTBCs through convolutional coded SVD systems. Similar to
BICMB-FP, BICMB-PC achieves both full diversity and full multiplexing, and its
performance is almost the same as BICMB-FP. The advantage of BICMB-PC is that
it can provide a much lower decoding complexity than BICMB-FP, since the real
and imaginary parts of the received signal can be separated for BICMB-PC of
dimensions 2 and 4, and only the part corresponding to the coded bit is
required to acquire one bit metric for the Viterbi decoder.Comment: accepted to conference; Proc. IEEE ICC 201
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