951 research outputs found
On robust multiuser detection
We study the design of multiuser detectors from an H∞ point of view. The H∞ approach is most appropriate in the situations where the statistical properties of the disturbances are not known or are too hard to model and analyze. The design of the H∞ optimal FIR multiuser detectors can be efficiently performed using numerical methods. Exploiting the inherent non-uniqueness of the H∞ solution, we additionally optimize for an average performance thus obtaining mixed H^2/H∞ optimal multiuser detector. Recursive solutions, allowing for computationally efficient implementation of the decision-feedback detectors, is briefly discussed
On the Performance of Mismatched Data Detection in Large MIMO Systems
We investigate the performance of mismatched data detection in large
multiple-input multiple-output (MIMO) systems, where the prior distribution of
the transmit signal used in the data detector differs from the true prior. To
minimize the performance loss caused by this prior mismatch, we include a
tuning stage into our recently-proposed large MIMO approximate message passing
(LAMA) algorithm, which allows us to develop mismatched LAMA algorithms with
optimal as well as sub-optimal tuning. We show that carefully-selected priors
often enable simpler and computationally more efficient algorithms compared to
LAMA with the true prior while achieving near-optimal performance. A
performance analysis of our algorithms for a Gaussian prior and a uniform prior
within a hypercube covering the QAM constellation recovers classical and recent
results on linear and non-linear MIMO data detection, respectively.Comment: Will be presented at the 2016 IEEE International Symposium on
Information Theor
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
A Linear Multi-User Detector for STBC MC-CDMA Systems based on the Adaptive Implementation of the Minimum-Conditional Bit-Error-Rate Criterion and on Genetic Algorithm-assisted MMSE Channel Estimation
The implementation of efficient baseband receivers characterized by affordable computational load is a crucial point in the development of transmission systems exploiting diversity in different domains. In this paper, we are proposing a linear multi-user detector for MIMO MC-CDMA systems with Alamouti’s Space-Time Block Coding, inspired by the concept of Minimum Conditional Bit-Error-Rate (MCBER) and relying on Genetic-Algorithm (GA)-assisted MMSE channel estimation. The MCBER combiner has been implemented in adaptive way by using Least-Mean-Square (LMS) optimization. Firstly, we shall analyze the proposed adaptive MCBER MUD receiver with ideal knowledge of Channel Status Information (CSI). Afterwards, we shall consider the complete receiver structure, encompassing also the non-ideal GA-assisted channel estimation. Simulation results evidenced that the proposed MCBER receiver always outperforms state-of-the-art receiver schemes based on EGC and MMSE criterion exploiting the same degree of channel knowledge (i.e. ideal or estimated CSI)
Large-System Analysis of Multiuser Detection with an Unknown Number of Users: A High-SNR Approach
We analyze multiuser detection under the assumption that the number of users
accessing the channel is unknown by the receiver. In this environment, users'
activity must be estimated along with any other parameters such as data, power,
and location. Our main goal is to determine the performance loss caused by the
need for estimating the identities of active users, which are not known a
priori. To prevent a loss of optimality, we assume that identities and data are
estimated jointly, rather than in two separate steps. We examine the
performance of multiuser detectors when the number of potential users is large.
Statistical-physics methodologies are used to determine the macroscopic
performance of the detector in terms of its multiuser efficiency. Special
attention is paid to the fixed-point equation whose solution yields the
multiuser efficiency of the optimal (maximum a posteriori) detector in the
large signal-to-noise ratio regime. Our analysis yields closed-form approximate
bounds to the minimum mean-squared error in this regime. These illustrate the
set of solutions of the fixed-point equation, and their relationship with the
maximum system load. Next, we study the maximum load that the detector can
support for a given quality of service (specified by error probability).Comment: to appear in IEEE Transactions on Information Theor
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