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For multiple-antenna systems, the technologies of joint symbol and channel parameter estimation have been developed in recent works. However, existing technologies have a number of problems, such as performance degradation and the large cost of prior information. In this paper, a tensor space-time coding scheme in multiple-antenna systems was consi...
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... In practice, this a priori information can be obtained simply by using one pilot-symbol sent from each relay to the destination. A similar procedure was adopted in other works [11,47,52,53] in the context of relaying systems. For the DCNTD system, the ambiguity removal is implemented after Steps 3 and 7 of Algorithm 1, while for the DCNPD system, the ambiguity removal is carried out after Steps 3, 4, and 8 of Algorithm 2. The scalar ambiguity associated with Step 4 of Algorithm 2 can be estimated by exploiting the fact that one of the factors of this KPF is a matrix composed of ones. ...
Coupled tensor decompositions have emerged as a promising approach to analyze large dimensional datasets in the context of signal processing applications. In this paper, the general concept of doubly coupled decomposition (DCD) for high-order tensors is first proposed, extending the idea of coupled decompositions to doubly coupled nested structures which result from the contraction of two sets of tensors, each set depending on a specific mode. Two new decompositions are defined, the so- called doubly coupled nested Tucker decomposition (DCNTD) and doubly coupled nested PARAFAC decomposition (DCNPD). Uniqueness of these DCDs is analyzed. In a second part, we show how these DCDs can be used to model multirelay multicarrier MIMO cooperative communication networks with two different tensor codings at the source and relay nodes. Exploiting the multilinear structure of the received signals and assuming the coding tensors are known at the destination, semi-blind closed form receivers are developed for jointly estimating the channels and transmitted symbols. The proposed receivers use
Khatri-Rao and Kronecker product factorization algorithms. Identifiability conditions for system parameter estimation and design of the tensor codes are also addressed. Monte Carlo simulation results illustrate the performance improvement of the proposed DCD-based systems over existing state-of-the-art ones.
... The work [26] presents a robust semiblind receiver based on the Tucker2 model for joint symbol and channel estimation. Notably, the semi-blind receiver in [26] can be developed into the multi-user massive MIMO system. ...
... In addition, 64QAM is adopted to modulate information symbols. The channel matrices H 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ) 0 3 6 9 12 15 18 21 24 27 30 TP-ALS Receiver Iter1_Average 22 17 14 11 11 11 11 11 12 12 13 Iter1_Max 59 45 32 25 19 18 18 15 18 20 21 Iter2_Average 6 5 4 4 4 3 3 3 3 3 3 Iter2_Max 16 10 6 5 5 4 4 4 Iter1_Average 15 13 11 9 8 8 8 8 9 9 10 Iter1_Max 40 34 29 18 12 11 11 11 12 12 13 Iter2_Average 2 2 2 2 2 2 2 2 2 2 2 Iter2_Max 2 2 2 2 2 2 2 2 2 scaling ambiguities as in [26] and [38]. δ th = 1 × 10 −4 is chosen for the proposed TP-ALS receiver. ...
Massive multiple-input multiple-output (MIMO) relay can significantly improve the capacity and throughput of wireless networks, thus has been a sought-after technique for future communication systems. However, the development of massive MIMO relay systems faces several major challenges. For example, the knowledge of instantaneous channel state information (CSI) is needed to estimate signals and optimize systems. Traditional estimation schemes need to transmit pilot sequences, which occupy the spectrum resources. In this paper, we propose a tensor-based method for joint signal and channel estimation for multi-user massive MIMO relay systems without using pilot sequences, and develop two tensor-based semi-blind receivers. Through multidimensional signaling scheme, the signals received by each user are formulated as the block Tucker2-PARAFAC (TP) tensor model. Then, two semi-blind receivers are proposed to jointly estimate the information signals and channel matrices. One is based on the tensor-based closed-form receiver, the other is based on the tensor-based iterative receiver. The proposed closed-form approach can also be used to initialize the iterative receiver for improving the convergence speed. In particular, the proposed schemes are practicable for both time division duplexing (TDD) and frequency division duplexing (FDD) modes. Uniqueness, identifiability and complexity are analyzed for our receivers. Compared with existing receivers, our receivers offer superior bit error rate (BER) and normalized mean square error (NMSE) performance. Numerical examples are shown to demonstrate the effectiveness of the proposed tensor-based receivers.
... The advantages of this method are that it has low system complexity and improves the nonlinear effect of radio over fiber link. A robust semi-blind receiver combined with the Tucker-2 model was proposed in [21]. The proposed receiver has high spectral efficiency and can be used in multi-user massive MIMO systems. ...
The multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is the combination of the OFDM and MIMO technologies, which could improve the system capacity and make efficient utilization of the frequency spectrum. This paper utilizes space-time block coding (STBC) to achieve diversity gains and combat the channel fading. However, channel estimation is an essential block for space-time block decoding (STBD). Many channel estimation methods are utilized for the single antenna OFDM system, but they cannot be directly applied to the multiple antennas system due to the interference from other antennas. In this paper, orthogonal pilot sequences are designed to suppress the interference of pilot symbols from other transmit antennas. This paper also derives a minimum mean square error (MMSE) channel estimation method in MIMO-OFDM systems. The MMSE method involves the inverse operation of the channel autocorrelation matrix, which has a large calculation complexity. To further reduce the complexity of the MMSE method, the singular value decomposition (SVD) is used to decompose the channel autocorrelation matrix, which avoids the inverse operation. Simulation results verify that the SVD channel estimation method with comb-type pilots and STBC can be effectively adapted to multipath propagation conditions.
