Research Article  Open Access
Xianwen He, Gaoqi Dou, Jun Gao, "Individual Channel Estimation in a Diamond Relay Network Using RelayAssisted Training", International Journal of Digital Multimedia Broadcasting, vol. 2017, Article ID 1320689, 8 pages, 2017. https://doi.org/10.1155/2017/1320689
Individual Channel Estimation in a Diamond Relay Network Using RelayAssisted Training
Abstract
We consider the training design and channel estimation in the amplifyandforward (AF) diamond relay network. Our strategy is to transmit the source training in timemultiplexing (TM) mode while each relay node superimposes its own relay training over the amplified received data signal without bandwidth expansion. The principal challenge is to obtain accurate channel state information (CSI) of secondhop link due to the multiaccess interference (MAI) and cooperative data interference (CDI). To maintain the orthogonality between data and training, a modified relayassisted training scheme is proposed to migrate the CDI, where some of the cooperative data at the relay are discarded to accommodate relay training. Meanwhile, a couple of optimal zerocorrelation zone (ZCZ) relayassisted sequences are designed to avoid MAI. At the destination node, the received signals from the two relay nodes are combined to achieve spatial diversity and enhanced data reliability. The simulation results are presented to validate the performance of the proposed schemes.
1. Introduction
To combat the effects of multipath fading in wireless networks, relay cooperative communication is proposed to generate a virtual multipleantenna network by sharing antennas [1, 2]. This topic has been the subject of intensive research due to its potential for providing spatial diversity and coverage extension without the limitation of hardware complexity [3]. The diamond relay network is one of the special cases of the multiplerelay networks. Specifically, with only two relays utilized, the diamond relay network can yield diversity benefits to combat fading and be simple enough to design the transmission protocol and efficient scheduling [4]. Moreover, as compared to threenode network with a single relay utilized, the most advantage of the diamond relay network is that different relays can transmit and receive at the same time, which in turn translates to gains due to spatial reuse [5]. Many applications practical in diamond relay network have been studied, for example, the optimal power allocation [6, 7], the optimal opportunistic relay scheme [8–11], and the optimal position selection of relays [4, 5]. In essence, a diamond relay network is a cascade of two singlehop links consisting of the broadcast channel and the multipleaccess channel. However, purely knowing the cascaded channel is insufficient to support the above optimal design in diamond relay network. As a result, channel estimation problems are generally more challenging in diamond relay networks than in threenode relay networks.
With regard to an amplifyandforward (AF) based relay cooperation system, most research has focused on acquiring the cascaded channel state information (CSI). The authors in [12] designed a cyclicorthogonal training sequence and presented a practical estimation algorithm for a cascaded channel. In [13], the authors derived necessary and sufficient conditions for a relay amplifying matrix in MultiInput MultiOutput (MIMO) systems. However, the schemes presented in [12, 13] cannot be adopted for estimating an individual channel. For most applications, the CSI of an individual link is indispensable at the receiver to perform signal retrieving and system optimization [14, 15]. Recently, it has been shown in [16] that a multiuser receiver can be used to blindly estimate the channel matrices associated with both individual links. Unfortunately, it is difficult to obtain an instantaneous CSI. The subsequent work in [17] had expanded the superimposed training scheme presented in [18] to the area of multirelay networks. The authors derived optimal pilot symbol designs including both modification diagonal matrix and relay superimposed pilot symbol. Nevertheless, the orthogonal constraint among the source pilot, superimposed pilot, and the modification matrix demands an additional spending and a protocol to coordinate the relaypilot sequence and modification matrix with the special form of orthogonality, which adds additional complexity to power allocation and joint optimization.
A typical example is the AFbased diamond relay network consisting of source node (SN), destination node (DN), and two AF halfduplex relay nodes with no direct link between the source and the destination [19]. In this paper, we consider an AFbased diamond relay network and propose a modified relayassisted training strategy to estimate secondhop link. Our strategy is to transmit the source training and data in timemultiplexing (TM) mode from the SN, while each relay superimposes its specialized relay training over the amplified received data vectors. In this manner, the complex problem of a joint optimization design of source training and relay training is disassembled into two independent optimization design problems. In the work, two methods are employed to eliminate cooperative data interference (CDI) and multiaccess interference (MAI). Firstly, to remove the effects of the unknown cooperative informationinduced interference on the estimator of secondhop links, some cooperative informationbearing data tones of the received signal at each relay node are discarded to accommodate relay training sequence to keep the orthogonality between data and training. Meanwhile, a simple iterative reconstruction method is employed to compensate the distortion at the DN. Secondly, we derive a couple of optimal zerocorrelation zone (ZCZ) relayassisted sequences designed to eliminate the MAI and minimize the MSE when estimating the secondhop (, ) channel [20, 21]. At the DN, the received signals from each relay node are combined to achieve spatial diversity and enhance data reliability. The simulation results are presented to prove the performance of the proposed schemes.
The rest of the paper is organized as follows. In Section 2, we present the AFbased diamond relay network. The design of the training sequences at relay nodes is described in Section 3. The detection performance and iterative reconstruction method are introduced in Section 4. The simulations results are presented in Section 5 and conclusions are drawn in Section 6.
Notation 1. Superscripts , , and denote the complex conjugate transpose, transpose, and pseudoinverse, respectively. The × identity matrix is denoted by . The Discrete Fourier Transform (DFT) of the × vector is denoted by , where has entry .
2. System Model
We consider a singlecarrier transmission in the AFbased diamond relay network operating in a frequencyselective fading environment, where the data is transmitted from the SN to the DN through two relay nodes , as shown in Figure 1. The individual channel impulse response links are defined as , , . Individual channel taps are independent and Rayleighdistributed as , . The signal transmission between and can be partitioned into two time slots. We consider a cyclic prefix (CP) singlecarrier transmission system and assume perfect synchronization for both transmission phases.
At the SN, we first design a frame consisting of the training block and the data block , where denotes the block length. To avoid interblock interference at both relay nodes and the DN, a CP of a length is inserted into the front of each block before transmission and is removed after reception. During the first time slot, the SN transmits one data block to each relay nodes with average power of .
At the second time slot, and amplify the received signal and forward the signal to with average power of , respectively. In our strategy, relayassisted training is superimposed onto the top of the amplified data vector as illustrated in Figure 2. Due to MAI, cannot identify the corresponding relay channels. Therefore, linear modification diagonal matrix is designed for training signal at each relay.
After the signal processing at each relay node, the retransmitted signal is given by, respectively,where is an circulant matrix, with the first columns , and , are circular complex white Gaussian noise with zero mean and covariance matrix , that is allocated to the relay training, that is, , and is allocated to the amplified received data, where . The modification diagonal matrix is set, where and the amplified factors are given bywhere . The received signal at the DN can be expressed bywhere is an circulant matrix, with the first columns , , are circular complex white Gaussian noise, , with , . And , , , are also circular complex white Gaussian noise.
3. Design of the Relay Training Sequence
With the purpose of signal retrieving and obtaining diversity, individual channel estimation is required in a diamond relay network. From (4), the received data signal can be expressed as follows:where is an columnwise circulant matrix with the first column . It is noted that includes the equivalent noise that is related to the specific realization of , the extra CDI term , and the MAI term . Assuming that the received data, training sequence, and noise in (5) are mutually independent, the covariance matrices of can be written, respectively, aswhere , with . Without loss of generality, we take the estimation of as an example, and the traditional Least Square (LS) estimation of the secondhop channel is obtained byThe MSE of the LS estimator can be given byFor an positive definite matrix , we have , where the equality holds if and only if for some nonzero constant . Using this and the fact that the matrix is positive definite, the optimal training for a fixed power must satisfy the following conditions:and the Minimum MeanSquare Error (MSE) of the LS estimation is given by
The MSE of the secondhop channel in (10) consists of three terms. The first term is related to the CDI, the second term is related to the MAI, and the third term is related to the equivalent noise, all of which have serious effects on the estimate of the MSE.
(A) Cooperative Data Interference and RelayPropagated Noise Cancellation. To eliminate the effects of the unknown CDI and relaypropagated noise on the estimator of the secondhop links, it is crucial to perform signal preprocessing at each relay node. Because relayassisted training is periodic and its energy is concentrated only at the equispaced frequency pins, we discard some cooperative data tones of the received signal at each relay node so that the DFT at the specific frequency pins is identically zero. In this way, we construct an orthogonal structure between the cooperative data and the relayassisted training. In fact, the discarded cooperative data tones include both the CDI and the relayintroduced noise terms. At each relay node, we subtract the vector from the received signal , where . Here, we define an diagonal matrix in the frequency domain withAssume that the indices of the nonzero pilot tones corresponding to and belong to . The data signal at can be refreshed as follows:
(B) Multiaccess Interference Cancellation. With the aim of minimizing the MSE of secondhop channel, the following constrained conditions and should be satisfied according to (7):
In Scheme A, a couple of relay training sequences ( and ) with their energy concentrated at different frequency pins is designed. We assume that the indices of the nonzero pilot tones corresponding to belong to , while the indices of the nonzero pilot tones corresponding to belong to . The condition should be satisfied. With pilot tones occupying the disjoint frequency tones, the problem of MAI can be solved. However, it is necessary to perform signal preprocessing at each relay node. As a result, to maintain the orthogonality, it is indispensable that the frequency components of the received signal corresponding to both and should be removed to accommodate relaypilot tones as illustrated in Figure 3. Clearly, the distortion of the received data would be doubled in Scheme A and the detection performance will be seriously degraded. Therefore, this is not an appropriate scheme to avoid MAI.
In Scheme B, a couple of relay training with autocorrelation and crosscorrelation properties are designed. In the ZCZ sequence group , denotes the period of the ZCZ sequence, denotes the length of the zerocorrelation zone, and the condition is required, where and is an integer. The autocorrelation and crosscorrelation properties of the ZCZ sequence are stated as follows:where . The relay training sequences can satisfy the conditions and denoted in (13). Moreover, the indices of the nonzero pilot tones corresponding to the ZCZ sequence group including and both belong to . As a result, only the frequency components corresponding to should be removed. This is the optimal scheme to avoid MAI. The structure of the data block in the frequency domain is illustrated in Figure 4.
At the DN, the decoupling of the two relay training sequences is easily realized. In essence, the complicated twoaccess channel estimation problem is decomposed into two independent channel estimation problems. According to (5) and (12), the received data signal at the DN can be refreshed as follows:where . It is noted that the partial cooperative data vector [the second term in (15)] and the relay training vector [the third term in (15)] occupy the disjoint frequency tones, respectively. As a result, the cooperative data have no effect on the estimation of the secondhop channel. According to C1, the pseudoinverse of can be denoted as
In the LS estimation, according to and , the improved estimation of the secondhop channel is obtained byAccording to (17), the relaypropagated noise introduced from each relay node is removed thoroughly. As both the CDI and MAI are removed, the minimum MSE of the improved LS estimation is given by
4. Diversity Combining and Iterative Reconstruction
With the purpose of signal retrieving and obtaining diversity, individual channel ( and ) estimation is required in diamond relay networks. With the estimated secondhop channels and , we employ the LS channel estimation in [12] to obtain estimated channels and . After removing the contribution of the relay training sequences from by simply computing , the DFT of can be written asThe equalized signal is given bywhere , in the case of Minimum MeanSquare Error (MMSE) equalization, , are the DFT of the estimated channels and , is the DFT of equivalent noise , and . Because the data distortion is singular, cannot be recovered linearly. As a result, we employ the symboltosymbol iterative detection scheme. The initial hard detector of is given bywhere stands for the decision function. For subsequent iterations, the detected symbols are utilized to compute and compensate for the data loss. The detected symbols at the next iteration are given by
5. Simulation Results
In this section, we present the simulation results to evaluate performance of both the proposed scheme (denoted as “proposed” in figures) and the pilotbased scheme in [15] (denoted as “pilotbased scheme” in figures) in terms of bit error rate (BER) and normalized MSE of channel estimation. The channel is randomly generated and assumed to be uncorrelated Rayleigh fading with a length of . For ease of description, the block lengths are the same in this context. However, different lengths can be used based on the requirements. The equispaced and equipowered pilots are selected. The data symbols are extracted from the quadrature phaseshift keying constellation. The relay training is designed using , and the relay training sequences are set as shown in Table 1 according to [20, 21]. SNR of each link are assumed that , where . In the simulation, we employ various system parameters of frame (Case and Case ), respectively, as shown in Table 2.


To obtain the optimal design for the relay training sequences, we display the bit error rate (BER) performance versus for the proposed scheme under different signaltonoise ratio (SNR) conditions in Figure 5. Clearly, the BER performance improves when increases. This occurs because more power is allocated to the superimposed relay training, while less power is allocated to the data sequence. It is evident that the optimal remains at the minimum value when the SNR is 10, 15, and 20, respectively. As a result, we adopt as the approximately optimal value.
Figure 6 shows the MSE performance of channel estimation with different schemes for Case . The estimation performance of the link is worse than that of the link because the former link is affected by both the propagated noise introduced from the relay nodes and the estimation error of the secondhop link. Moreover, the performance of the proposed scheme is superior to that of the pilotbased scheme described in [17]. The pilotbased scheme aims to superimpose the relayassisted training onto the source training signal with a special orthogonal constraint. With the same average power and different block length, the total energy of the relayassisted training in the scheme [17] is reduced compared to the proposed scheme. Moreover, the relaypropagated noise has a serious impact on the estimation of the link in the scheme [17]. As a result, the proposed estimation scheme is superior to the scheme [17].
Figure 7 shows the BER performance with different schemes for Case . In the proposed Scheme B, the performance improvement via the first iteration is distinct but negligible with additional iterations. It can be observed that the BER performance of the Scheme B is much better than that of Scheme A and for the scheme described in [17]. It is noted that a double distortion is induced in Scheme A and the inferior channel estimation of the pilotbased scheme clearly results in a degradation in BER. It can be predicted that a symbol error floor will occur in both Scheme A and the scheme described in [17] with an increasing SNR. After two iterations, the detection performance in Scheme B can approach that with perfect CSI, which means the channel estimation satisfies the need of the cooperative system. The performance reduction caused by the distortion can be judged in terms of the BER loss in the figure.
Figures 8 and 9 show the MSE performance and BER performance with different schemes for Case . We obviously observe that the estimation performance is almost the same for Case and Case . Moreover, the BER performance for the pilotbased scheme would remain the same with various system parameters of frame. However, with the length of data block () increasing, the BER performance in the proposed scheme get a certain improvement. It is because that the distortion on every symbol is reduced with increasing, when the fixed distortion is scattered throughout the data block. As a result, when we employ the system parameter of larger frames, the proposed scheme is more competitive.
6. Conclusions
In the AFbased diamond relay network, a novel relayassisted training strategy is proposed to acquire the individual CSI. In our strategy, each relay node superimposes its own special training sequence over the amplified received data, which can be used to acquire the secondhop CSI. To solve the interference problems of unknown cooperative data, we discarded some cooperative data at each relay to accommodate the relaypilot tones. Meanwhile, we derive a couple of relay training with autocorrelation and crosscorrelation properties to decouple the combined relay training sequences. The simulations show that the channel estimation performance of the proposed scheme is superior to that of the pilotbased scheme described in [17]. Moreover, the detecting performance improvement via the iteration reconstruction is distinct.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant no. 61302099) and China Postdoctoral Science Foundation (Grant no. 2015T81107).
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Copyright © 2017 Xianwen He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.