3,329 research outputs found
Efficient Optimal Joint Channel Estimation and Data Detection for Massive MIMO Systems
In this paper, we propose an efficient optimal joint channel estimation and
data detection algorithm for massive MIMO wireless systems. Our algorithm is
optimal in terms of the generalized likelihood ratio test (GLRT). For massive
MIMO systems, we show that the expected complexity of our algorithm grows
polynomially in the channel coherence time. Simulation results demonstrate
significant performance gains of our algorithm compared with suboptimal
non-coherent detection algorithms. To the best of our knowledge, this is the
first algorithm which efficiently achieves GLRT-optimal non-coherent detections
for massive MIMO systems with general constellations.Comment: 5 pages, 4 figures, Conferenc
Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
In this paper, we provide for the first time a systematic comparison of
distribution matching (DM) and sphere shaping (SpSh) algorithms for short
blocklength probabilistic amplitude shaping. For asymptotically large
blocklengths, constant composition distribution matching (CCDM) is known to
generate the target capacity-achieving distribution. As the blocklength
decreases, however, the resulting rate loss diminishes the efficiency of CCDM.
We claim that for such short blocklengths and over the additive white Gaussian
channel (AWGN), the objective of shaping should be reformulated as obtaining
the most energy-efficient signal space for a given rate (rather than matching
distributions). In light of this interpretation, multiset-partition DM (MPDM),
enumerative sphere shaping (ESS) and shell mapping (SM), are reviewed as
energy-efficient shaping techniques. Numerical results show that MPDM and SpSh
have smaller rate losses than CCDM. SpSh--whose sole objective is to maximize
the energy efficiency--is shown to have the minimum rate loss amongst all. We
provide simulation results of the end-to-end decoding performance showing that
up to 1 dB improvement in power efficiency over uniform signaling can be
obtained with MPDM and SpSh at blocklengths around 200. Finally, we present a
discussion on the complexity of these algorithms from the perspective of
latency, storage and computations.Comment: 18 pages, 10 figure
Low-complexity dominance-based Sphere Decoder for MIMO Systems
The sphere decoder (SD) is an attractive low-complexity alternative to
maximum likelihood (ML) detection in a variety of communication systems. It is
also employed in multiple-input multiple-output (MIMO) systems where the
computational complexity of the optimum detector grows exponentially with the
number of transmit antennas. We propose an enhanced version of the SD based on
an additional cost function derived from conditions on worst case interference,
that we call dominance conditions. The proposed detector, the king sphere
decoder (KSD), has a computational complexity that results to be not larger
than the complexity of the sphere decoder and numerical simulations show that
the complexity reduction is usually quite significant
Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code
The 3D MIMO code is a robust and efficient space-time block code (STBC) for
the distributed MIMO broadcasting but suffers from high maximum-likelihood (ML)
decoding complexity. In this paper, we first analyze some properties of the 3D
MIMO code to show that the 3D MIMO code is fast-decodable. It is proved that
the ML decoding performance can be achieved with a complexity of O(M^{4.5})
instead of O(M^8) in quasi static channel with M-ary square QAM modulations.
Consequently, we propose a simplified ML decoder exploiting the unique
properties of 3D MIMO code. Simulation results show that the proposed
simplified ML decoder can achieve much lower processing time latency compared
to the classical sphere decoder with Schnorr-Euchner enumeration
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