ABSTRACT:.In this paper **channel** **estimation** for orthogonal frequency division multiplexing (OFDM) is presented. This **channel** **estimation** employs two training symbol in combination with polynomial fitting thus to get accurate **estimation** result. **Channel** **estimation** is mainly performed by sending pilot from the transmitter and measuring the pilot at the receiver side. A sufficient amount of pilot needs to be transmitted in order for the receiver to obtain a reasonably accurate estimate of the **channel** response. Simulation result is also represented. Simulation makes the study of OFDM processing very easy. By simply taking the values of SNR, we can easily observe .

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Successful development of broadband over powerline is obviously a potential solution for wired communication sys- tems with the existence of the powerline network. From past research, it is known that the powerline **channel** suffers from multipath fading, frequency selectivity and also impulsive noise. Multi Carrier Code Division Multiple Access (MC-CDMA) is a promising solution for an impulsive noise powerline **channel**. This paper starts with the MC-CDMA transmitter structure and focuses on powerline **channel** model, noise model and various types of available **channel** esti- mators. The main concern in Powerline Communication Systems is the existence of impulsive noise. The proposed pilot assisted **channel** **estimation** uses the modified least square estimator that reduces the effect of impulsive noise in the estimated **channel** impulse response.

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OFDM transforms the frequency-selective fading channels into parallel flat fading sub channels, as long as the cyclic prefix (CP) inserted at the beginning of each OFDM symbol is longer than or equal to the **channel** length. The **channel** length means the length of impulse response of the **channel** as discrete sequence. The signals on each subcarrier can be easily detected by a time-domain or frequency-domain .Otherwise the effect of frequency- selective fading cannot be completely eliminated, and inter- carrier interference (ICI) and inter-symbol interference (ISI) will be introduced in the received signal. **Channel** **estimation** techniques that could flexibly detect the signals in both cases and therefore **channel** **estimation** is important in MIMO-OFDM systems.

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In this thesis, a full review of fading channels model (Rayleigh & Rice), LS estimator and MMSE estimator is given. Rayleigh fading model is happens when no LOS path exists in between transmitter and receiver, but only have indirect path than the resultant signal received at the receiver will be the sum of all the reflected and scattered waves. Rician fading model will be appeared once the receiver receives one strong component which will be a line of sigh signal. MMSE estimator has a better performance than LS estimator in order of average MSE and SNR but it has a computational complexity so it use only in the low SNR environments. However, MMSE estimator will be used in systems that require precise measurement and time sensitive environments such as high speed communications systems. On modulation side, the results show that the M-QAM has a better performance for both **channel** **estimation** algorithms than M-PSK modulation. Fig .6: the two techniques of **channel** **estimation** is used

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In this paper, we consider the **estimation** of sparse nonlinear communication channels. Transmission over the channels is represented by sparse Volterra models that incorporate the ef- fect of Power Amplifiers. **Channel** **estimation** is performed by compressive sensing methods. Efficient algorithms are pro- posed based on Kalman filtering and Expectation Maximiza- tion. Simulation studies confirm that the proposed algorithms achieve significant performance gains in comparison to the conventional non-sparse methods.

Multiple Input Multiple Output (MIMO) antenna systems are being given much attention to provide high capacity with less bandwidth requirement. In this paper, some **channel** **estimation** techniques have been tried to implement with the adaptive semiblind **channel** **estimation** scheme using less requirement of pilot symbols similar to as in the case of the estimating the **channel** with known **channel** state information(CSI) conditions with requirement of high **channel** bandwidth which is not required in this analysis. The improved results have been found with less requirement of **channel** bandwidth and compared with the already simulated results. It is shown that in addition to improving the spectral efficiency, the proposed technique offer better semiblind **channel** **estimation** accuracy for the partial CSI conditions.

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This article develops a semiblind **channel** **estimation** method for MIMO–OFDM systems based on a speciﬁc and non-redundant precoding scheme, say, circular pre- coding, since the circular precoding allows **channel** esti- mation at the receiver and simpliﬁes the encoding scheme at the transmitter [14]. In literature, to the best of our knowledge, only two circular precoding based methods have been proposed for single-input single-output (SISO) OFDM systems [14,15]. Thus the current study focuses on generalizing the methods in the SISO case [14,15] to the MIMO–OFDM systems. The proposed method is based on second-order statistics. With circular precoding at the transmitters, the autocorrelation matrix of the received data is equal to a noise-perturbated matrix involving the outer product of the **channel** frequency response matrix and the coeﬃcents relating to the precoding. Dividing each submatrix in the autocorrelation matrix by the cor- responding coeﬃcient related to the precoding gives a noise-perturbed outer product of the **channel** frequency response matrix. Then we use the relation of the chan- nel frequency response matrix and the **channel** impulse response matrix to transform the above noise-perturbed matrix to another noise-perturbed matrix. The result- ing noise-perturbed matrix is equal to an outer product of the **channel** impulse response matrix plus a diagonal matrix due to **channel** noise. Next, we use a simple method to eliminate the noise components to obtain the outer product of the **channel** impulse response matrix. Finally, the **channel** impulse response matrix is obtained by

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In order to achieve better performance of **channel** es- timation by using CS signal recovery algorithms, we should modify the position of the pilot placement in 2D plane. There is a important factors to consider in pilot placement modification. That is the modification does a little change to the LTE system. So we must considering the LTE pilot placement and frame structure. [14] Ex- press that CS-based **channel** **estimation** scheme can achieve better performance when use random pilot placement. In standard LTE pilot placement, every block has to provide four indexes to place pilot in twelve placements. So there is C4 12 combination. Through 1000 Monte carols simu- lation of random pilot placement; we find that the modi- fied pilot placement, shown in the right figure of Figure 2, can achieve the best performance of **channel** estima- tion.

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In this section, we present two sets of experiments to evaluate the **channel** **estimation** algorithm’s responsiveness to changes in the environment. In the first experiment, traffic generation is based on a Poisson on-off traffic source for WLAN and Blue- tooth. In the second experiment, we use more realistic traffic such as MPEG, voice, FTP and HTTP. Our simulation environ- ment is based on a detailed MAC, PHY and **channel** models for Bluetooth and IEEE 802.11 (WLAN) as described in [6]. The parameters used in the setup vary according to the exper- iment. The common simulation parameters are summarized in Table I. The simulations are run for 1800 seconds of simulated time unless specified otherwise. We run 10 trials using a dif- ferent random seed for each trial. In addition, to plotting the mean value, we verify that that the statistical variation around the mean values are very small (less than 1%).

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For broadband channels, in [17], a two-slot pilot-assisted CE scheme for ANC was presented. In the first slot, both users transmit their pilots to the relay, where one of the pilot signals is cyclically shifted [18] to allow the relay to separate and estimate the CSIs from both users. This stage is named multiple-input single-output **channel** **estimation** (MISO-CE) due to its analogy to multiple-input multiple- output (MIMO) OFDM systems [18]. During the second slot, the relay broadcast its pilot signal to the users, which estimate the corresponding CSIs. This stage is named single- input single-output **channel** **estimation** (SISO-CE). We note here that only BER performance has been evaluated by computer simulation in [17]. Therefore, in this work, we focus our attention to investigate and analyze the achievable performance of low-complexity pilot-assisted CE for broad- band ANC in a frequency-selective fading **channel**.

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This project aims at simulation of a simple and most efficient **channel** **estimation** method and a good modulation technique for increasing the **channel** capacity, bandwidth, increasing bit rates and eliminates inter symbol interference. There are well-known training based **channel** **estimation** methods are; Zero forcing, Minimum Mean Square **Estimation** (MMSE), Alamouti code. The main aim is to reduce the computational complexity of **channel** **estimation** using different algorithm and implementing 2x2 MIMO system using BPSK and QPSK modulation technique. Fig-2 shows the block diagram of the project.

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degradation of the output SINR of the MRT precoder, around 0.5dB, which is then considerably increased to 4dB when the high-level reciprocity error introduced. The ZF precoded system with imperfect **channel** **estimation** suffers more from the reciprocity errors, such that more than 10 dB SINR loss can be experienced in the case with the high-level reciprocity error, compared with the degraded performance caused by the **estimation** error only. In addition, the results in Fig. 7 and 8 can be considered in selecting suitable modulation schemes for the practical massive MIMO system in the presence of different levels of the reciprocity error and the **estimation** error. We can now generalise the conclusion at the end of Sec- tion V-A1 by taking the imperfect **channel** **estimation** into account, and summarise that the MRT precoded system can be more robust to both reciprocity and **channel** **estimation** errors compared with the ZF precoded system.

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Using the received baseband signal, the receiver implements an iterative MUD with **channel** **estimation**. Going through the receiver structure, as shown in Fig. 1, an initial pilot based estimate is first obtained. Using this, the receiver implements a per-symbol parallel interference canceling elementary signal estimator (PIC-ESE)[4]. The ESE, as detailed in Appendix A, models the interference plus noise as a complex Gaussian process and produces extrinsic log-likelihood ratio (LLR) outputs of the transmitted code bits. After the ESE, the peruser LLR streams are deinterleaved and despread before being fed to the soft-input soft- output (SISO) decoders. The extrinsic information output of the decoders are then respread and reinterleaved before fed back to the ESE and to the second stage of the **channel** **estimation** process. The soft symbols are then used to update the **channel** estimates and LLRs. The **channel** **estimation** process is divided into two parts, one pilot based, and one decision-directed.

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The rest of the paper is organized as follows. First, we study the **channel** **estimation** error and the cost of compu- tation of the MIMO system under consideration. Next, we describe the generalized energy reduction scheme. After this, we focus on minimizing energy at the transmitter and the receiver separately. Next, we consider joint transmitter and receiver energy minimization. To illustrate our method, we consider a MIMO system with flat-fading channels of arbi- trary size and give comparisons of energy and error variation for diﬀerent **channel** **estimation** schemes obtained by varying the number of active transmit/receive antennas under a fixed delay and error constraint.

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period and 9 OFDM frames’ data must be stored in the memory. Comparing Figure 17 with Figure 15 and Figure 18 with Figure 16, it is obvious that MMSE + LI performs much worse under the same **channel** model and pilot pattern, es- pecially in the high SNR region. Our algorithms are CRLB achievable, whereas MMSE + LI is not. Furthermore, there is an error floor for MSE in fast time-varying channels us- ing MMSE + LI. In order to remove it, more pilot symbols in the time domain must be inserted. This will reduce the system spectrum eﬃciency. Another advantage of our algo- rithms is that they do not have demodulation latency except the processing time because our algorithms are based on the received signals of the current OFDM frame. MMSE + LI or other pilot-symbols-assisted **channel** **estimation** methods have some extent of demodulation latency as long as they ap- ply some kinds of time-domain interpolation or filtering.

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In block-type pilot-based **channel** **estimation** we are going to estimate the **channel** conditions (specified by or ) given the pilot signals (specified by matrix X or vector ). The received signals (specified by ), with or without using certain knowledge of the **channel** statistics. In this the **estimation** can be based on least square (LS), minimum mean-square error (MMSE), and modified MMSE.

5.2 Pilot allocation in sparsity-based **estimation** methods In this part, we compare the MSE and perfect recon- struction percentage in **channel** **estimation** for pilot allo- cation methods presented in this article. For our simulations in this part, we generated a random 3-tap **channel** with varying fading parameters in each OFDM block and averaged the results over 5000 runs. Figure 4 shows the MSE of the estimated **channel** for two differ- ent methods of pilot allocation. In the first scenario, the pilots are chosen uniformly at random for each block; in our proposed scheme, the pilots are arranged according to a (73,9,1) cyclic difference set and its cyclic shifts for different OFDM blocks. The MSE of the structured LS estimator is also presented in the figures as CRB-S to give us a meaningful goal standard. This bound is given by [9]:

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