1,225 research outputs found
Performance Analysis of Iterative Channel Estimation and Multiuser Detection in Multipath DS-CDMA Channels
This paper examines the performance of decision feedback based iterative
channel estimation and multiuser detection in channel coded aperiodic DS-CDMA
systems operating over multipath fading channels. First, explicit expressions
describing the performance of channel estimation and parallel interference
cancellation based multiuser detection are developed. These results are then
combined to characterize the evolution of the performance of a system that
iterates among channel estimation, multiuser detection and channel decoding.
Sufficient conditions for convergence of this system to a unique fixed point
are developed.Comment: To appear in the IEEE Transactions on Signal Processin
Multiuser Detection by MAP Estimation with Sum-of-Absolute-Values Relaxation
In this article, we consider multiuser detection that copes with multiple
access interference caused in star-topology machine-to-machine (M2M)
communications. We assume that the transmitted signals are discrete-valued
(e.g. binary signals taking values of ), which is taken into account as
prior information in detection. We formulate the detection problem as the
maximum a posteriori (MAP) estimation, which is relaxed to a convex
optimization called the sum-of-absolute-values (SOAV) optimization. The SOAV
optimization can be efficiently solved by a proximal splitting algorithm, for
which we give the proximity operator in a closed form. Numerical simulations
are shown to illustrate the effectiveness of the proposed approach compared
with the linear minimum mean-square-error (LMMSE) and the least absolute
shrinkage and selection operator (LASSO) methods.Comment: submitted; 6 pages, 7 figure
Multiuser MIMO-OFDM for Next-Generation Wireless Systems
This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base stationβs or radio portβs coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems
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
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