Block-wise frequency offset estimation scheme in MIMO-OFDM systems

In this new information age, high data rate and strong reliability features our wireless communication systems and is becoming the dominant factor for a successful deployment of commercial networks. MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing), a new wireless broadband technology, has gained great popularity for its capability of high rate transmission and its robustness against multi-path fading and other channel impairments. A major challenge to MIMO-OFDM systems is how to obtain the channel state information accurately and promptly for coherent detection of information symbols and channel synchronization. In the first part, this dissertation formulates the channel estimation problem for MIMO-OFDM systems and proposes a pilot-tone based estimation algorithm. A complex equivalent base-band MIMO-OFDM signal model is presented by matrix representation. By choosing equally-spaced and equally-powered pilot tones from sub-carriers in one OFDM symbol, a down-sampled version of the original signal model is obtained. Furthermore, this signal model is transformed into a linear form solvable for the LS (least-square) estimation algorithm. Based on the resultant model, a simple pilot-tone design is proposed in the form of a unitary matrix, whose rows stand for different pilot-tone sets in the frequency domain and whose columns represent distinct transmit antennas in the spatial domain. From the analysis and synthesis of the pilot-tone design in this dissertation, our estimation algorithm can reduce the computational complexity inherited in MIMO systems by the fact that the pilot-tone matrix is essentially a unitary matrix, and is proven an optimal channel estimator in the sense of achieving the minimum MSE (mean squared error) of channel estimation for a fixed power of pilot tones. In the second part, this dissertation addresses the wireless location problem in WiMax (worldwide interoperability for microwave access) networks, which is mainly based on the MIMO-OFDM technology. From the measurement data of TDOA (time difference of arrival), AOA (angle of arrival) or a combination of those two, a quasi-linear form is formulated for an LS-type solution. It is assumed that the observation data is corrupted by a zero-mean AWGN (additive white Gaussian noise) with a very small variance. Under this assumption, the noise term in the quasi-liner form is proved to hold a normal distribution approximately. Hence the ML (maximum-likelihood) estimation and the LS-type solution are equivalent. But the ML estimation technique is not feasible here due to its computational complexity and the possible nonexistence of the optimal solution. Our proposed method is capable

Ambient backscatter communication (ABC) is a promising technology for future wireless systems due to its ultra-low-power operation and simplicity. To further enhance its performance, multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) has been adopted in this work. However, carrier frequency offset (CFO) can disrupt the orthogonality of the MIMO-OFDM signal, which significantly degrades the performance of the reader’s receiver. To address this issue, we consider an asynchronous ABC system and propose a deep learning (DL) algorithm to estimate and compensate the CFO at the reader, ensuring reliable communication in MIMO-OFDM-based ABC systems. The proposed architecture integrates a hybrid attention mechanism, which captures inter-dependencies between the two types of extracted features, and a squeeze-and-excitation network that adaptively refines channel-wise feature responses. The proposed architecture can also work for conventional OFDM and MIMO-OFDM systems. Furthermore, to validate the model’s robustness and generalization capability, additional test datasets are generated for various modulation schemes and different 3GPP TR38.901 tapped delay line channel models. The simulation results indicate its outperformance over existing approaches and has lower computational complexity than the existing methods.

To address the problem that in the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, the Compressed Sensing (CS) greedy algorithm uses the inner product criterion in the process of selecting atoms for channel estimation will lose part of the original signal information and affect the accuracy of reconstruction, this paper uses the Dice criterion instead of the inner product criterion for atom screening, the Dice criterion can better preserve the original state of the atoms. Due to the problem that the fixed step size of the Sparsity Adaptive Matching Pursuit (SAMP) algorithm affects the accuracy of channel estimation, this paper proposes an improved SAMP algorithm by using the idea of power function variable step size. The simulation results show that the improved SAMP algorithm has better Mean Square Error (MSE) and Bit Error Rate (BER) estimation performance than the traditional compressed sensing greedy algorithm in MIMO-OFDM system.

In orthogonal frequency division multiplexing (OFDM) systems, since virtual subcarriers are not used for transmission, approach of conventional uniformly placed pilot tones is not applicable in some situations. In this letter, based on nonuniform pilot tone placement, we derive optimal pilot sequences, which can achieve the minimum mean square error of the least squares estimate for multiple-input multiple-output (MIMO) OFDM systems. Simulation results demonstrate the effectiveness of the proposed approach.

Rapid development of wireless services, leads to ubiquitous personal connectivity in the world. The demand for multimedia interactivity is higher in the world which leads to the requirement of high data transmission rate. Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a future wireless service which is used to overcome the existing service problems such as development of subscriber pool and higher throughput per user. Although it overcomes the problems in existing services, resource allocation becomes one of the major issues in the MIMO-OFDM systems. Resource allocation in MIMO-OFDM is the optimization of subcarrier and power allocation for the user. The overall performance of the system can be improved only with the efficient resource allocation approach. The user data rate is increased by efficient allocation of the subcarrier and power allocation for each user at the base station, which is subject to constraints on total power and bit error rate. In this study, the problem of resource allocation in MIMO-OFDM system is tackled using hybrid artificial bee colony optimization algorithm based on a crossover operation along with Poisson-Jensen in equation. The experimental results show that the proposed methodology is better than the existing techniques.

The MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing) system recently received considerable attention because of its potential ability to enable the higher transmission data rate while keeping the high signal quality. In practical MIMO-OFDM systems, the efficient and highly accurate channel estimation method is the mandatory requirement to achieve the potential system performance over MIMO channels. This paper proposes the discrete cosine transform (DCT) interpolation-based channel estimation (DCTI-CE) method, which could meet the above requirements. The salient feature of the proposed DCTI-CE method is to enable the estimation of all link channel responses with high accuracy by using only one preamble symbol even under the severe multi-path fading channel for wireless LAN systems.

In this paper, we analyze the Bit Error Rate (BER) performance of Golden coded Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system over Rician multipath fading channel. We also compare the performance of the MIMO-OFDM system using Golden code in Rayleigh and Rician multipath fading channels. We discuss the effects of the presence of line-of-sight (LoS) component in the multipath fading environment which renders the improvement in the overall performance of the Golden coded MIMO-OFDM. This paper discusses the performance of Golden codes in a frequency selective Rician fading channel. To deal with the frequency selective fading channel, we use the OFDM (Orthogonal Frequency Division Multiplexing) modulation. The BER performance of the Golden coded MIMO-OFDM system under several propagation conditions has been evaluated. Keywords—MIMO, OFDM, Golden STBC, Rayleigh fading, Rician fading.

Publication in the conference proceedings of EUSIPCO, Viena, Austria, 2004

This paper proposes a Group Detection (GD) algorithm with Max-Log-MAP Sphere Decoder (MLM-SD) in order to reduce the complexity of signal detection in a Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system. The proposed algorithm divides spatial streams into multiple partial spatial streams by using Minimum Mean Square Error (MMSE) detector, followed by multiple MLM-SDs with reduced number of spatial streams. Although the spatial diversity gain in the MLM-SD degrades because of the lack of the degrees of freedom exploited by the MMSE detector, its diversity gain is recovered by combining the metrics obtained by the multiple MLM-SDs. In a MIMO wireless LAN multipath fading environment, the complexity of the proposed algorithm is 10% of that of the original MLM-SD and the performance degradation in terms of SNR is slightly less than that of the original MLM-SD in 4-by-4 MIMO architecture with 64QAM achieving 216 Mbps. It is also found that the proposed algorithm is robust against the limitation of the number of searches in sphere decoder.

Differential unitary space-time modulation (DUSTM) has emerged as a promising technique to obtain spatial diversity without intractable channel estimation. This paper presents a study of the application of DUSTM on multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with frequency-selective fading channels. From the view of a correlation analysis between subcarriers of OFDM, we obtain the maximum achievable diversity of DUSTM on MIMO-OFDM systems. Moreover, an efficient implementation strategy based on subcarrier reconstruction is proposed, which transmits all the signals of one signal matrix in one OFDM transmission and performs differential processing between two adjacent OFDM blocks. The proposed method is capable of obtaining both spatial and multipath diversity while reducing the effect of time variation of channels to a minimum. The performance improvement is confirmed by simulation results.

Accurate channel estimation is crucial for millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Conventional grid-based compressive sensing approaches encounter the base mismatch problem, which degrades channel estimation accuracy. To address this issue, this letter proposes a novel gridless channel estimation strategy tailored for mmWave MIMO-OFDM systems. Specifically, the channel estimation problem is formulated as a joint sparse signal recovery problem by exploiting the inherent sparsity in the angle-delay domain of the mmWave channel. We then introduce a Hankel-Toeplitz block model-based new atomic norm minimization (NANM) algorithm with multiple measurement vectors (MMV), representing the formulated problem as a semi-definite programming (SDP) problem with structured sparsity. To efficiently solve the SDP problem, we employ a low-complexity alternating direction multiplier method (ADMM). Simulation results verify that the proposed method significantly enhances channel estimation accuracy with reduced pilot overhead, compared with conventional channel estimation techniques.

This paper proposes a new interference cancellation scheme for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in the presence of intersymbol interference (ISI). The proposed interference cancellation scheme uses the estimated channel coefficients and the channel state information (CSI) to reproduce the interference components, then iteratively cancel them from the received signals. The so-called remodulation technique is also included in this scheme to enhance the interference cancellation performance. To illustrate the proposed method, we assume that the MIMO-OFDM system does not use a guard interval (spectral efficiency factor eta = 1). Simulation results show that the performance of system in terms of the symbol error ratio (SER) is significantly improved by applying one iteration of the proposed canceller. After two iterations, the SER is slightly better than the results obtained if the guard interval (GI) length is sufficient (eta = 0.87).

In this paper, a two stage hybrid interference cancellation and equalization framework is proposed for interference cancellation in the uplink of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The first stage uses time domain equalization to suppress cochannel interference, mitigate asynchronism, and shorten the postequalization channel response to be no longer than the length of the cyclic prefix. The second stage performs low-complexity single tap equalization and detection in the frequency domain. The framework is developed specifically for spatial multiplexing and is applied to multiuser MIMO-OFDM systems with asynchronism between users as well as to single-user MIMO-OFDM systems. Various equalizer design methods are proposed that determine the coefficients directly from the training data and are compared with methods based on channel estimates. The equalizer coefficients and postequalization channel response are found by solving a joint optimization that maximizes the signal to interference-plus-noise ratio (SINR) in the frequency domain. Simulations compare various training-based methods and show the proposed methods provide good bit error rate (BER) and SINR performance in a variety of interference scenarios.

This paper proposes a new channel estimation method for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in the presence of intersymbol interference (ISI). The proposed method uses special training sequences (TS) mainly for two reasons. Firstly, to obtain a desirable crest-factor of the transmitted training signal. Secondly, to eliminate the influence of ISI on the channel estimation performance. By using the recommended training sequences, one can avoid the ill-conditioned least square (LS) problem. To illustrate the proposed method, we assume that the system does not use a guard interval (GI). Simulation results show that the proposed method outperforms conventional methods with respect to the mean square error (MSE) of the estimated channel by more than 20 dB if the signal-to-noise ratio (SNR) is high.

Cognitive radio technology enables spectrum sensing (SS), which allows the secondary user (SU) to access vacant frequency bands in the periods when the primary user (PU) is not active. Due to its minute implementation complexity, the SS approach based on energy detection (ED) of the PU signal has been analyzed in this paper. Analyses were performed for detecting PU signals by the SU in communication systems exploiting multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission technology. To perform the analyses, a new algorithm for simulating the ED process based on a square-law combining (SLC) technique was developed. The main contribution of the proposed algorithm is enabling comprehensive simulation analyses of ED performance based on the SLC method for versatile combinations of operating parameter characteristics for different working environments of MIMO-OFDM systems. The influence of a false alarm on the detection probability of PU signals impacted by operating parameters such as the signal-to-noise ratios, the number of samples, the PU transmit powers, the modulation types and the number of the PU transmit and SU receive branches of the MIMO-OFDM systems have been analyzed in the paper. Simulation analyses are performed by running the proposed algorithm, which enables precise selection of and variation in the operating parameters, the level of noise uncertainty and the detection threshold in different simulation scenarios. The presented analysis of the obtained simulation results indicates how the considered operating parameters impact the ED efficiency of symmetric and asymmetric MIMO-OFDM systems.