anning Yu

PARAFAC-Based Blind Estimation Of Possibly Underdetermined Convolutive MIMO Systems

In this paper, we consider the problem of blind identification of a convolutive multiple-input-multiple-output (MIMO) system with No outputs and Ni inputs. While many methods have been proposed to blindly identify convolutive MIMO systems with No ges Ni (overdetermined), very scarce results exist for the case of (underdetermined), all of which refer to systems that either have some special structure or special and values. In this paper, we show that, as long as , independent of whether the system is overdetermined or underdetermined, we can always find the appropriate order of statistics that guarantees identifiability of the system response within trivial ambiguities. We also propose an algorithm to reach the solution, that consists of parallel factorization (PARAFAC) of a -way tensor containing th-order statistics of the system outputs, followed by an iterative scheme. For a certain order of statistics , we provide the description of the class of identifiable MIMO systems. We also show that this class can be expanded by applying PARAFAC decomposition to a pair of tensors instead of one tensor. The proposed approach constitutes a novel scheme for estimation of underdetermined systems, and improves over existing approaches for overdetermined systems.

Blind MIMO System Estimation Based on PARAFAC Decomposition of Higher Order Output Tensors

We present a novel framework for the identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. Samples of the system frequency response are obtained based on parallel factorization (PARAFAC) of three- or four-way tensors constructed based on, respectively, third- or fourth-order cross spectra of the system outputs. The main difficulties in frequency-domain methods are frequency-dependent permutation and filtering ambiguities. We show that the information available in the higher order spectra allows for the ambiguities to be resolved up to a constant scaling and permutation ambiguities and a linear phase ambiguity. Important features of the proposed approach are that it does not require channel length information, needs no phase unwrapping, and unlike the majority of existing methods, needs no prewhitening of the system outputs

Blind Estimation of Multiple Carrier Frequency Offsets

Multiple carrier-frequency offsets (CFO) arise in a distributed antenna system, where data are transmitted simultaneously from multiple antennas. In such systems the received signal contains multiple CFOs due to mismatch between the local oscillators of transmitters and receiver. This results in a time-varying rotation of the data constellation, which needs to be compensated for at the receiver before symbol recovery. This paper proposes a new approach for blind CFO estimation and symbol recovery. The received base-band signal is over-sampled, and its polyphase components are used to formulate a virtual multiple-input multiple-output (MIMO) problem. By applying blind MIMO system estimation techniques, the system response is estimated and used to subsequently transform the multiple CFOs estimation problem into many independent single CFO estimation problems. Furthermore, an initial estimate of the CFO is obtained from the phase of the MIMO system response. The Cramer-Rao lower bound is also derived, and the large sample performance of the proposed estimator is compared to the bound.

Parafac Based Blind Estimation Of Mimo Systemswith Possibly More Inputs Than Outputs

We consider the problem of frequency domain identification of a convolutive multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed in Y. Yu and A.P. Petropulu (2005) that uses PARAFAC decomposition of a tensor that is formed based on third-order statistics of the system output. The approach of Y. Yu and A.P. Petropulu (2005) utilizes only one slice of the output tensor to recover one row of the system response matrix. We here propose an approach that fully exploits the information in the output tensor. As a result, the proposed method not only achieves lower error values but also becomes applicable to MIMO systems with possibly more inputs than outputs. By combing two output tensors, we can make the approach applicable to more systems. We also extend the method to employ fourth-order statistics of the system output

Blind identification of distributed antenna systems with multiple carrier frequency offsets

In spatially distributed multiuser antenna systems, the received signal contains multiple carrier-frequency offsets (CFOs) arising from mismatch between the oscillators of transmitters and receivers. This results in a time-varying rotation of the data constellation, which needs to be compensated at the receiver before symbol recovery. In this paper, a new approach for blind CFO estimation and symbol recovery is proposed. The received base-band signal is over-sampled, and its polyphase components are used to formulate a virtual multiple-input multiple-Output (MIMO) problem. By applying blind MIMO system estimation techniques, the system response can be estimated and decoupled versions of the user symbols can be recovered, each one of which contains a distinct CFO. By applying a decision feedback phase lock loop (PLL), the CFO can be mitigated and the transmitted symbols can be recovered. The estimated MIMO system response provides information about the CFOs that can be used to initialize the PLL, speed up its convergence, and avoid ambiguities usually linked with PLL.

Linearly precoded OFDM system with adaptive modulation

We consider the linearly precoded OFDM approach proposed by A.P. Petropulu et al. (see IEEE Trans. Wireless Commun., 2003), where a non-redundant precoding was applied to the symbol blocks before entering the OFDM system. The precoding, while it maintained the transmit power, introduced a structure to the transmitted signal that allowed for blind channel estimation by a simple auto-correlation performed at the receiver. We propose an adaptive modulation based extension of the method of Petropulu et al. in order to combat a channel with deep fading. Bits are allocated on each subcarrier so that the overall transmit power is minimized under a fixed bit error rate (BER). The obtained bit allocation can also be viewed as minimizing BER for the precoded system, under a fixed overall transmit power constraint. The proposed approach provides large performance gains over the uniformly loaded one, especially under deep fading conditions, for the same overall throughput and transmit power.

Improved PARAFAC Based Blind MIMO System Estimation

We consider the problem of frequency domain identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed by the authors [1] that uses PARAFAC decomposition of a tensor that is formed based on higher-order statistics of the system output. The approach of [1] utilizes only one slice of the output tensor at each time. We here proposed an approach that fully exploits the information in the output tensor, and as a result achieves lower error values. The proposed approach can also be applied to systems with more inputs than outputs.

Reduced Complexity Blind Estimation of Under-Determined Convolutive Mimo Systems

We consider identification of an under-determined convolutive multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In our recent work, we showed that an N i-input and No-output system can be estimated within trivial ambiguities based on PARAFAC decomposition of a tensor containing K-th order statistics of the system output, where Kgesmax{(2Ni-1)/(No-1), 3}. In this paper we show that by using a tensor pair we can guarantee identifiability while using statistics of order smaller than in the single tensor case. We also provide an iterative identification scheme. The proposed tensor-pair approach results in complexity reduction as it involves lower dimensionality tensors and lower order statistics

Blind Estimation of a Class of Under-Determined Convolutive MIMO Systems Using Parafac Decomposition of Output Tensors

We consider the problem of frequency domain identification of a convolutive multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed that uses PARAFAC decomposition of a tensor that is formed based on third-order statistics of the system output. The approach utilizes only one slice of the output tensor to recover one row of the system response matrix. We here propose an approach that fully exploits the information in the output tensor. As a result, the proposed method not only achieves lower error values but also becomes applicable to a class of MIMO systems with more inputs than outputs. We also show how by using higher order statistics, i.e., fourth- or sixth-order statistics, or pairs of third or Fourth-order tensors, one can further expand the class of under-determined systems that are identifiable.

Improved single PARAFAC decomposition based blind MIMO system estimation

We consider the problem of frequency domain identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed by the authors that uses PARAFAC decomposition of a tensor that is formed based on higher-order statistics of the system output. The approach of Y. Yu and A.P. Petropulu, 2005, utilizes only one slice of the output tensor to recover one row of the system response matrix. We proposed an approach that fully exploits the information in the output tensor, and as a result achieves lower error values. The proposed modification renders the method applicable to systems with more inputs than outputs