1,697 research outputs found
Application of Heuristic Algorithms for Multiuser Detection
In this paper we propose application of heuristic algorithms in multiuser detection (MUD). The proposed algorithm combines a tabu search heuristic algorithm with a local search heuristic algorithm. The new proposed structure brings several improvements when compared to both the conventional (matched filter) detector and the decorrelating detector. Additionally, the algorithm proposed here fairly approximates the performance of the optimal MUD detector with much reduced computational complexity
Receiver Architectures for MIMO-OFDM Based on a Combined VMP-SP Algorithm
Iterative information processing, either based on heuristics or analytical
frameworks, has been shown to be a very powerful tool for the design of
efficient, yet feasible, wireless receiver architectures. Within this context,
algorithms performing message-passing on a probabilistic graph, such as the
sum-product (SP) and variational message passing (VMP) algorithms, have become
increasingly popular.
In this contribution, we apply a combined VMP-SP message-passing technique to
the design of receivers for MIMO-ODFM systems. The message-passing equations of
the combined scheme can be obtained from the equations of the stationary points
of a constrained region-based free energy approximation. When applied to a
MIMO-OFDM probabilistic model, we obtain a generic receiver architecture
performing iterative channel weight and noise precision estimation,
equalization and data decoding. We show that this generic scheme can be
particularized to a variety of different receiver structures, ranging from
high-performance iterative structures to low complexity receivers. This allows
for a flexible design of the signal processing specially tailored for the
requirements of each specific application. The numerical assessment of our
solutions, based on Monte Carlo simulations, corroborates the high performance
of the proposed algorithms and their superiority to heuristic approaches
NOMA and interference limited satellite scenarios
This paper deals with the problem of non-orthogonal multiple access (NOMA) in multibeam satellite systems, where the signals are jointly precoded. It is considered that the number of frames that are simultaneously transmitted is higher than the number of feeds, reducing the precoding interference mitigation capabilities as the system becomes overloaded. In order to solve this problem, we assume that the satellite user terminals are able to perform multi-user detection to mitigate the interference. In the current NOMA approach, it
is assumed a successive interference cancellation (SIC) receiver.
To increase the spectral efficiency, this paper investigates NOMA
with simultaneous non-unique detection (SND). Compared to the
case where user terminals perform single user detection (SUD),
conventional scheduling heuristic rules do not longer apply in this
scenario. Therefore, different scheduling algorithms are proposed
considering both SIC and SND strategies. As the numerical evaluations show, SND yields larger average data rates than the SIC receiver. Concerning the scheduling, the best strategy is to pair users with highly correlated channels and the lowest channel gain difference. It is also shown that the sum-rate can be increased in overloaded satellite systems with respect to satellite scenarios, where the number of transmitted frames and feeds is the same.Peer ReviewedPostprint (author's final draft
Quantum search algorithms, quantum wireless, and a low-complexity maximum likelihood iterative quantum multi-user detector design
The high complexity of numerous optimal classic communication schemes, such as the maximum likelihood (ML) multiuser detector (MUD), often prevents their practical implementation. In this paper, we present an extensive review and tutorial on quantum search algorithms (QSA) and their potential applications, and we employ a QSA that finds the minimum of a function in order to perform optimal hard MUD with a quadratic reduction in the computational complexity when compared to that of the ML MUD. Furthermore, we follow a quantum approach to achieve the same performance as the optimal soft-input soft-output classic detectors by replacing them with a quantum algorithm, which estimates the weighted sum of a functionâs evaluations. We propose a soft-input soft-output quantum-assisted MUD (QMUD) scheme, which is the quantum-domain equivalent of the ML MUD. We then demonstrate its application using the design example of a direct-sequence code division multiple access system employing bit-interleaved coded modulation relying on iterative decoding, and compare it with the optimal ML MUD in terms of its performance and complexity. Both our extrinsic information transfer charts and bit error ratio curves show that the performance of the proposed QMUD and that of the optimal classic MUD are equivalent, but the QMUDâs computational complexity is significantly lower
Symbol Error Rate Performance of Box-relaxation Decoders in Massive MIMO
The maximum-likelihood (ML) decoder for symbol detection in large
multiple-input multiple-output wireless communication systems is typically
computationally prohibitive. In this paper, we study a popular and practical
alternative, namely the Box-relaxation optimization (BRO) decoder, which is a
natural convex relaxation of the ML. For iid real Gaussian channels with
additive Gaussian noise, we obtain exact asymptotic expressions for the symbol
error rate (SER) of the BRO. The formulas are particularly simple, they yield
useful insights, and they allow accurate comparisons to the matched-filter
bound (MFB) and to the zero-forcing decoder. For BPSK signals the SER
performance of the BRO is within 3dB of the MFB for square systems, and it
approaches the MFB as the number of receive antennas grows large compared to
the number of transmit antennas. Our analysis further characterizes the
empirical density function of the solution of the BRO, and shows that error
events for any fixed number of symbols are asymptotically independent. The
fundamental tool behind the analysis is the convex Gaussian min-max theorem
Composite CDMA - A statistical mechanics analysis
Code Division Multiple Access (CDMA) in which the spreading code assignment
to users contains a random element has recently become a cornerstone of CDMA
research. The random element in the construction is particular attractive as it
provides robustness and flexibility in utilising multi-access channels, whilst
not making significant sacrifices in terms of transmission power. Random codes
are generated from some ensemble, here we consider the possibility of combining
two standard paradigms, sparsely and densely spread codes, in a single
composite code ensemble. The composite code analysis includes a replica
symmetric calculation of performance in the large system limit, and
investigation of finite systems through a composite belief propagation
algorithm. A variety of codes are examined with a focus on the high
multi-access interference regime. In both the large size limit and finite
systems we demonstrate scenarios in which the composite code has typical
performance exceeding sparse and dense codes at equivalent signal to noise
ratio.Comment: 23 pages, 11 figures, Sigma Phi 2008 conference submission -
submitted to J.Stat.Mec
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