Direction of arrival (DOA) estimation is an antenna array signal processing technique used in, for instance, radar and sonar systems, source localization, and channel state information retrieval. As new applications and use cases appear with the development of next generation mobile communications systems, DOA estimation performance must be continually increased in order to support the nonstop growing demand for wireless technologies. In previous works, we verified that a deep neural network (DNN) trained offline is a strong candidate tool with the promise of achieving great on-grid DOA estimation performance, even compared to traditional algorithms. In this paper, we propose new techniques for further DOA estimation accuracy enhancement incorporating signal-to-noise ratio (SNR) prediction and an end-to-end DOA estimation system, which consists of three components: source number estimator, DOA angular spectrum grid estimator, and DOA detector. Here, we expand the performance of the DOA detector and angular spectrum estimator, and present a new solution for source number estimation based on DNN with very simple design. The proposed DNN system applied with said enhancement techniques has shown great estimation performance regarding the success rate metric for the case of two radio wave sources although not fully satisfactory results are obtained for the case of three sources.

In this paper, we propose a received signal interpolation method for enhancing the performance of multiple signal classification (MUSIC) algorithm. In general, the performance of the conventional MUSIC algorithm is very sensitive to signal-to-noise ratio (SNR) of the received signal. When array elements receive the signals with nonuniform SNR values, the resolution performance is degraded compared to elements receiving the signals with uniform SNR values. Hence, we propose a signal calibration technique for improving the resolution of the algorithm. First, based on original signals, rough direction of arrival (DOA) estimation is conducted. In this stage, using frequency-domain received signals, SNR values of each antenna element in the array are estimated. Then, a deteriorated element that has a relatively lower SNR value than those of the other elements is selected by our proposed scheme. Next, the received signal of the selected element is spatially interpolated based on the signals received from the neighboring elements and the DOA information extracted from the rough estimation. Finally, fine DOA estimation is performed again with the calibrated signal. Simulation results show that the angular resolution of the proposed method is better than that of the conventional MUSIC algorithm. Also, we apply the proposed scheme to actual data measured in the testing ground, and it gives us more enhanced DOA estimation result.

In this paper the SNR estimation is performed frame by frame, during the speech activity. For this purpose, the fourth-order moments of the real and imaginary parts of frequency components are extracted, for both the speech and noise, separately. For each noisy frame, the mentioned fourth-order moments are also estimated. Making use of the proposed formulas, the signal-to-noise ratio is estimated in each frequency index of the noisy frame. These formulas also predict the overall signal-to-noise ratio in each noisy frame. What makes our method outstanding compared to conventional approaches is that this method takes into consideration both the speech and noise identically. It estimates the negative SNR almost as well as the positive SNR.

By using multiple repeated signal replicas to formulate the accumulative observed noisy signal sequence (AONSS) or the differential observed noisy signal sequence (DONSS) in the hybrid ARQ system, a novel data-aided maximum likelihood (DA ML) SNR estimation and a blind ML SNR estimation technique are proposed for the AWGN channel. It is revealed that the conventional DA ML estimate is a special case of the novel DA ML estimate, and both the proposed DA ML and the proposed blind ML SNR estimation techniques can offer satisfactory SNR estimation without introducing significant additional complexity to the existing hybrid ARQ scheme. Based on the AONSS, both the generalized deterministic and the random Cramer-Rao lower bounds (GCRLBs), which include the traditional Cramer-Rao lower bounds (CRLBs) as special cases, are also derived. Finally, the applicability of the proposed SNR estimation techniques based on the AONSS and the DONSS are validated through numerical analysis and simulation results.

In OFDM receivers, an accurate signal-to-noise ratio (SNR) estimation is desirable for all sorts of operations involved in high-performance baseband demodulation. In this work, conventional SNR estimation techniques are investigated. Next, a blind SNR estimation scheme for the phase-shift keying (PSK) signals based on the coherence function is proposed. The proposed method is non-data-aided (NDA) and hence bandwidth-efficient. Simulations confirm that the proposed method has the best performance from moderate to high SNRs both in AWGN and dispersive channels; however, the proposed method has worse performance when SNRs are low.

In this letter, we aim at the study of an OFDM system that employs adaptive modulation techniques to achieve efficient transmission service. In adaptive modulation techniques, a banded AM technique can reduce the number of adaptive modulation information bits and signaling overhead. We evaluate the banded AM technique and compare with Subcarrier AM technique. Moreover, to evaluate the channel condition, we apply the SNR estimation method. Furthermore, to increase the performance of OFDM system, frequency domain pre-equalization is applied. The simulation results have been shown that the OFDM system with adaptive schemes can achieve the efficient transmission for multi-media services.

In this letter, we provide a performance evaluation of a Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM) with adaptive modulation in a multipath fading channel. The MB-OFDM system in conjunction with adaptive modulation technique can be considered as one of the solutions for severely degraded channels such as those common in frequency selective fading environments.

In this letter, we propose an FFT-based SNR estimation method for wireless OFDM systems, and analyze the impact of the proposed SNR estimation method on adaptive OFDM performance in slow Rayleigh fading channels. Numerical and simulation results show that the proposed method is effective and feasible for adaptive modulation in slow Rayleigh fading channels.

In this letter, we propose the SNR estimator for multipath fading channels. We employ the least squares estimator for estimating the channel and estimate the SNR using the estimated noise variance. The SNR estimation can be used to adapt the demodulation algorithm to enhance its performance, as well as to provide the channel quality information. Simulation results show the performance of SNR estimator.

This paper proposes a SNR estimation scheme based on Gibbs sampler. This scheme can estimate SNR using a very short received sequence, and does not require any prior information of the transmitted symbol. Compared with the existing estimators, the performance of this method is better when real SNR is larger than 5 dB in both single path channel and multi-path channel.

In this letter, we propose joint frequency offset and SNR estimation technique. The frequency offset may degrade the system performance greatly by deleterious effect. The proposed frequency offset estimation technique estimates frequency offset by employing the interpolation technique in the frequency domain. Also we propose SNR estimation technique using the estimated frequency offset for FFT-based system. The SNR estimated by the receiver can be used to adapt the demodulation algorithm to enhance its performance, as well as to provide the channel quality information. Simulation results show the performance of frequency offset and SNR estimator.

This letter discusses the performance of online SNR estimation including fading parameter estimation for parallel combinatorial SS (PC/SS) systems. The PC/SS systems are partial-code-parallel multicode SS systems, which have high-rate data transmission capability. Nakagami-m distribution is assumed as fading channel model to cover a wide range of fading conditions. The SNR and fading parameter estimation considered in this letter is based on only a statistical ratio of correlator outputs at the receiver. Numerical results show that SNR estimation performance with fading parameter estimation is close to the one in the case of perfect fading parameter information, if the number of transmitting PN codes is less than a half of assigned PN codes.

In this paper, we perform some experiments to show that the quantization noise caused by low-bit-rate speech coding can be characterized as a white noise process. Then, the signal-to-quantization noise ratio of the decoded speech for a given bit-rate is estimated by observing the perceptual speech quality equivalent to the artificially generated noisy speech obtained by adding a white Gaussian noise source. This information is incorporated into the parameter tuning of a noise-robust compensation algorithm for speech recognition so that the compensation algorithm can be performed better under a range of the estimated SNRs. Finally, we apply the compensation algorithm to a connected digit string recognition system that utilizes speech signals decoded by the GSM adaptive multi-rate (AMR) speech coder. It is shown that the noise-robust compensation algorithm reduces word error rates by 15% or more at low bit-rate modes of the AMR speech coder.

Power Line Communication (PLC) is very attractive for achieving high-rate in-home networks. Noise in power lines is modeled as a cyclostationary Gaussian process. In order to achieve reliable communication using power lines, effective measures including error control techniques need to be taken against this particular noise. This paper focuses its attention on an effect of turbo codes on PLC. We adopt two noise environments for examining the effect in terms of BER performance. The result of the examination provides that turbo codes have enough capability to limit the effect of the noise. It also provides that the effect depends on size of a channel interleaver. Since an effective SNR estimation scheme should be required to apply turbo codes to PLC, we also examine the effect of two SNR estimation schemes in terms of BER performance.

In this paper, an online SNR estimator is proposed for parallel combinatorial SS (PC/SS) systems in Nakagami fading channels. The PC/SS systems are called as partial-code-parallel multicode DS/SS systems, which have the higher-speed data transmission capability comparing with conventional multicode DS/SS systems referred to as all-code-parallel systems. We propose an SNR estimator based on a statistical ratio of correlator outputs at the receiver. The SNR at the correlator output is estimated through a simple polynomial from the statistical ratio. We investigate the SNR estimation accuracy in Nakagami fading channels through computer simulations. In addition, we apply it to the convolutional coded PC/SS systems with iterative demodulation and decoding to evaluate the estimation performance from the viewpoint of error rate. Numerical results show that the PC/SS systems with the proposed SNR estimator have superior estimation performance to conventional DS/SS systems. It is also shown that the bit error rate performance using our SNR estimation method is close to the performance with perfect knowledge of channel state information in Nakagami fading channels and correlated Rayleigh fading channels.