Maximum likelihood based estimation of frequency and phase offset in DCT OFDM systems under non-circular transmissions: algorithms, analysis and comparisons

Recently, the advantages of the discrete cosine transform (DCT) based orthogonal frequency-division multiplexing (OFDM) have come to the light. We thus consider DCT- OFDM with non-circular transmission (our results cover circular transmission as well) and present two blind joint maximum- likelihood frequency offset and phase offset estimators. Both our theoretical analysis and numerical comparisons reveal new advantages of DCT-OFDM over the traditional discrete Fourier transform (DFT) based OFDM. These advantages, as well as those already uncovered in the early works on DCT-OFDM, support the belief that DCT-OFDM is a promising multi-carrier modulation scheme

Precoder design for space-time coded systems over correlated Rayleigh fading channels using convex optimization

A class of computationally efficient linear precoders for space-time block coded multiple-input multiple-output wireless systems is derived based on the minimization of the exact symbol error rate (SER) and its upper bound. Both correlations at the transmitter and receiver are assumed to be present, and only statistical channel state information in the form of the transmit and receive correlation matrices is assumed to be available at the transmitter. The convexity of the design based on SER minimization is established and exploited. The advantage of the developed technique is its low complexity. We also find various relationships of the proposed designs to the existing precoding techniques, and derive very simple closed-form precoders for special cases such as two or three receive antennas and constant receive correlation. The numerical simulations illustrate the excellent SER performance of the proposed precoders

Improved Signal Detection for Ambient Backscatter Communications

In ambient backscatter communication (AmBC) systems, passive tags connect to a reader by reflecting an ambient radio frequency (RF) signal. However, the reader may not know the channel states and RF source parameters and can experience interference. The traditional energy detector (TED) appears to be an ideal solution. However, it performs poorly under these conditions. To address this, we propose two new detectors: (1) A joint correlation-energy detector (JCED) based on the first-order correlation of the received samples and (2) An improved energy detector (IED) based on the p-th norm of the received signal vector. We compare the performance of the IED and TED under generalized noise modeled using the McLeish distribution and derive a general analytical formula for the area under the receiver operating characteristic (ROC) curves. Based on our results, both detectors outperform TED. For example, the probability of detection with a false alarm rate of 1% for JCED and IED is 14% and 5% higher, respectively, compared to TED. These gains are even higher using the direct interference cancellation (DIC) technique, with increases of 16% and 7%, respectively. Overall, our proposed detectors offer better performance than the TED, making them useful tools for improving AmBC system performance.Comment: This paper has got Major Revision by IEEE TGC

Enhancing AmBC Systems with Deep Learning for Joint Channel Estimation and Signal Detection

The era of ubiquitous, affordable wireless connectivity has opened doors to countless practical applications. In this context, ambient backscatter communication (AmBC) stands out, utilizing passive tags to establish connections with readers by harnessing reflected ambient radio frequency (RF) signals. However, conventional data detectors face limitations due to their inadequate knowledge of channel and RF-source parameters. To address this challenge, we propose an innovative approach using a deep neural network (DNN) for channel state estimation (CSI) and signal detection within AmBC systems. Unlike traditional methods that separate CSI estimation and data detection, our approach leverages a DNN to implicitly estimate CSI and simultaneously detect data. The DNN model, trained offline using simulated data derived from channel statistics, excels in online data recovery, ensuring robust performance in practical scenarios. Comprehensive evaluations validate the superiority of our proposed DNN method over traditional detectors, particularly in terms of bit error rate (BER). In high signal-to-noise ratio (SNR) conditions, our method exhibits an impressive approximately 20% improvement in BER performance compared to the maximum likelihood (ML) approach. These results underscore the effectiveness of our developed approach for AmBC channel estimation and signal detection. In summary, our method outperforms traditional detectors, bolstering the reliability and efficiency of AmBC systems, even in challenging channel conditions.Comment: Accepted for publication in the IEEE Transactions on Communication

Joint CFO and channel estimation for ZP-OFDM modulated two-way relay networks

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for two-way relay network (TWRN). We consider the frequency selective fading channels and adopt the zero padding (ZP) based orthogonal frequency division multiplexing (OFDM) as the modulation of the transmission. Due to the mixture of the first and the second transmission phases, the joint estimation problem becomes much challenging than that in the traditional point-to-point communication systems. By introducing some redundancy, we modify the structure of ZP-OFDM to cope with non-zero frequency synchronization errors. We then propose a nulling-based least square (NLS) method for joint CFO and channel estimation. A detailed performance analysis of NLS has been conducted, where we prove that the unbiasedness of NLS and derive the closed-form estimation mean-square-error (MSE) at high signal-to-noise ratio (SNR). Finally, simulations are provided to corroborate the proposed studies. ©2010 IEEE.published_or_final_versionThe IEEE Conference on Wireless Communications and Networking (WCNC 2010), Sydney, NSW., 18-21 April 2010. In Proceedings of WCNC, 2010, p. 1-

Joint CFO and channel estimation for CP-OFDM modulated two-way relay networks

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for amplify-andforward (AF) two-way relay network (TWRN) that comprises two source terminals and one relay node. Both the system design and the estimation problem become more challenging when CFO is non-zero in a frequency-selective environment, as compared to the conventional point-to-point communication systems. By introducing some redundancy, we propose a cyclic prefix (CP) based OFDM modulation for TWRN that is capable of maintaining the advantage of using multi-carrier transmission and at the same time facilitates the system initialization, e.g., synchronization and channel estimation. We then apply a least square (LS) approach to solve the estimation problem. The approximated Cramér-Rao Bound (CRB) has been derived as the performance benchmark of the proposed estimator. Finally, simulations are provided to corroborate the theoretical studies. ©2010 IEEE.published_or_final_versionThe IEEE International Conference on Communications (ICC 2010), Cape Town, South Africa, 23-27 May 2010. In Proceedings of the IEEE International Conference on Communications, 2010, p. 1-