1,829 research outputs found
A new exact closest lattice point search algorithm using linear constraints
The problem of finding the closest lattice point arises in several communications scenarios and is known to be NP-hard. We propose a new closest lattice point search algorithm which utilizes a set of new linear inequality constraints to reduce the search of the closest lattice point to the intersection of a polyhedron and a sphere. This set of linear constraints efficiently leverage the geometric structure of the lattice to reduce considerably the number of points that must be visited. Simulation results verify that this algorithm offers substantial computational savings over standard sphere decoding when the dimension of the problem is large
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
Generalized feedback detection for spatial multiplexing multi-antenna systems
We present a unified detection framework for spatial multiplexing multiple-input multiple-output (MIMO) systems by generalizing Heller’s classical feedback decoding algorithm for convolutional codes. The resulting generalized feedback detector (GFD) is characterized by three parameters: window size, step size and branch factor. Many existing MIMO detectors are turned out to be special cases of the GFD. Moreover, different parameter choices can provide various performance-complexity tradeoffs. The connection between MIMO detectors and tree search algorithms is also established. To reduce redundant computations in the GFD, a shared computation technique is proposed by using a tree data structure. Using a union bound based analysis of the symbol error rates, the diversity order and signal-to-noise ratio (SNR) gain are derived analytically as functions of the three parameters; for example, the diversity order of the GFD varies between 1 and N. The complexity of the GFD varies between those of the maximum-likelihood (ML) detector and the zero-forcing decision feedback detector (ZFDFD). Extensive computer simulation results are also provided
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
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