In this work, the impact of the path delay differences in the various transmitter-receiver links on the channel capacity in an multiple-input multiple-output (MIMO) optical wireless communication (OWC) system with focus on line-of-sight (LOS) scenarios is investigated. Typically, the gain that can be achieved by using multiple transmit and receive elements in such a system is very limited, since the correlation between the different channels is extremely high. In the LOS channel, the magnitude of the transfer function is approximately constant for all frequencies. However, the different relative propagation times in the individual transmitter-receiver links lead to a varying frequency-dependent phase shift. So far, this aspect has been neglected in the most common optical free-space channel models, due to the relatively small path differences. In this paper, it is shown that even these small time delays have a significant impact on channel diversity for the highest modulation bandwidths reported in literature. With a single-sided modulation bandwidth of 1 GHz, the increase in normalized channel capacity due to the diversity caused by the different time delays is approximately 4 bps/Hz, depending on the geometry of the scenario. Therefore, accessing higher bandwidths not only increases the data throughput by shortening the symbol duration, but also enhances the potential gain from using MIMO techniques in a LOS OWC system.

This manuscript presents a novel blind modulation classification method applied to adaptive FBMC/OQAM systems which utilizes a neural network to combine different information types for distinguishing between different modulation formats. In the considered bit-loaded multicarrier transmission system, the receiver classifies the bit allocation table based on the received signal. Since the transmission channels from transmit and receive direction are correlated in time-division duplex systems, important side information about the used modulation schemes on each subcarrier is available, thus enabling a much more reliable classification. A deep learning-based algorithm is investigated numerically that extracts features from preprocessed data such as likelihood and receiver bandwidth efficiency values. Simulation results show a superior performance in typical wireless local area network scenarios compared to existing approaches.

In this paper, the reliability of spread Filter Bank Multicarrier (FBMC) Wireless Sensor Networks (WSNs) is analyzed for several off-tone jamming techniques to obtain a performance measure under intentional and unintentional interference. It is shown that FBMC is more robust against jamming signals with high Carrier Frequency Offset (CFO) than conventional Orthogonal Frequency Division Multiplexing (OFDM). Moreover, FBMC with Frequency Spreading (FS) shows a better performance than Multicarrier Code Division Multiple Access (MC-CDMA) when both are spread with a Walsh-Hadamard (W-H) code. When Time Spreading (TS) is applied, spread FBMC mostly outperforms MC-Direct Sequence (DS)-CDMA in terms of Bit Error Ratio (BER). A higher throughput can be also achieved when spread FBMC is implemented.

In this paper, a novel approach to detect and mitigate multi-tone interference (MTI) in a coded orthogonal frequency division multiplexing (OFDM) system based on deep neural networks (DNNs) is presented. The proposed approach utilizes the long short-term memory (LSTM) aiming to detect the indices of the jammed subcarriers within an OFDM symbol. Training data that is generated according to the statistical channel model is used for the offline training phase. The LSTMDNN is utilized as a classifier in the online deployment, where each subcarrier is classified as either jamming-free or jammed. The proposed approach based on LSTM-DNN requires no information about the interference characteristics. Simulation results show that the LSTM-DNN can learn and is able to classify the subcarriers with high accuracy compared to the traditional classification technique based on the energy detector (ED). In addition, simulation results demonstrate that the LSTMDNN is more robust to the variations in the wireless channel conditions compared to the ED classifier. As for the MTI mitigation technique, soft log-likelihood ratios (LLRs) weighting for the corresponding jammed subcarriers based on LSTM-DNN is proposed and compared with the traditional LLR erasing technique. Simulation comparison shows that the proposed LLR weighting can provide considerable performance enhancement.

In this paper, the performance of a Wireless Sensor and Actor Network (WSAN) based on the combination of Multicarrier Code Division Multiple Access (MC-CDMA) with Orthogonal Frequency Division Multiplexing (OFDM) and Offset Quadrature Amplitude Modulation (OQAM)-OFDM using the PHYDYAS prototype filter is investigated and compared under Barrage Noise Jamming (BNJ), Partial Band Jamming (PBJ), and Multi-tone Jamming (MTJ). Different spreading methods were applied to both transmission techniques. Under a Line-Of-Sight (LOS) indoor industrial channel with Additive White Gaussian Noise (AWGN), it is proven that CDMA-OQAM-OFDM variants outperform CDMA-OFDM in terms of Bit Error Ratio (BER) unless they use Time Spreading (TS) under the influence of MTJ.

Performance evaluations for heterogeneous communication technologies in the area of V2X safety applications for either improvement, comparison or combination purposes are in general focusing on the realistic representation of the upper communication stack layers, but therefore - often for the sake of simplicity - reducing the radio propagation channel to a maximum range model. The impact and hence the importance to model the environment dependent propagation effects in a representative manner has already been stressed in the literature several times - but separately for ad-hoc or cellular systems and not under the consideration of V2X safety-beaconing applications. By combining a realistic heterogeneous radio propagation channel model with a state-of-the-art V2X communication stack, a representative performance comparison of safety-relevant beaconing applications for 802.11p single-hop broadcast (SHB) and LTE Geocast can be conducted. Our simulation results show that the effects caused by the radio propagation channel cannot be neglected as they significantly impact key communication performance metrics such as channel gain, packet error ratio (PER) and channel load, where we primarily focus on the latter one to give further research directions for an efficient dissemination of safety-relevant V2X beacons.

This paper proposes a novel signaling-assisted maximum-a-posteriori (MAP)-based modulation classification (MC) algorithm for adaptive multiple- input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. For a high classification reliability, the MC method utilizes the received signal form, truncated signaling information and channel reciprocity in wireless time-division duplex (TDD) systems. Investigated in a typical indoor propagation scenario, numerical results reveal a superior performance of the proposed adaptive technique in terms of the effective bandwidth efficiency compared to conventional signaling-based adaptive and non- adaptive transmission methods.

This manuscript addresses the maximum-a-posteriori-(MAP)-based automatic modulation classification (AMC) for wireless orthogonal frequency division multiplexing (OFDM) systems with adaptive coding and modulation (ACM). The proposed classifier for quadrature amplitude modulation (QAM) schemes which utilizes the channel reciprocity in time-division duplex (TDD) systems requires the knowledge about the joint probabilities of the subcarrier-wise bit efficiencies at the transmitter and receiver side. In contrast to prior heuristic approaches, these probabilities are calculated analytically (with some approximations) if the transmitter and receiver apply the same bit loading algorithm on their erroneously estimated channel state information. Numerical results reveal that the precise knowledge of the joint probabilities improves the reliability of the automatic modulation classifier significantly, especially for high signal-to-noise power ratios (SNR).

In this paper, novel modulation classification algorithms for wireless orthogonal frequency division multiplexing (OFDM) systems with adaptive modulation and coding in time division duplex (TDD) mode are introduced. The proposed maximum-a-posteriori (MAP)-based classifiers exploit specific signalling bits which are transmitted along with the information data and can therefore be regarded as a combination of automatic modulation classification (AMC) and a pure signalling-based transmission concept. Simulations for indoor propagation scenarios are carried out to compare the performance of the new approach with existing algorithms in terms of the classification probability and the effective bandwidth efficiency.

This paper presents a novel adaptive transmission technique for time-division duplex (TDD)-based wireless orthogonal frequency division multiplexing (OFDM) systems which jointly optimizes the bit loading algorithm at the transmitter side and the modulation classification method at the receiver side. Instead of signaling the complete bit allocation table (BAT) to the receiver, a novel maximum-a-posteriori (MAP) modulation classification algorithm is applied to increase the effective data rate. Constraints on the bit loading algorithm aid to significantly increase the classification reliability while maintaining a high link quality and a low signaling overhead. Simulation results in terms of the classification reliability and the effective bandwidth efficiency reveal the superior performance of the proposed adaptive scheme for the transmission of short bursts in a typical indoor propagation scenario.

This paper presents a framework of maximum-a-posteriori (MAP) algorithms for the automatic modulation classification (AMC) in orthogonal frequency division multiplexing (OFDM) based communication systems with adaptive modulation (AM). The proposed classifiers intensively utilize side information typically available in wireless time-division duplex (TDD) systems that is channel reciprocity, the known frame structure and the knowledge about the total transmission data rate. As the computational complexity of the optimal algorithms is rather high, a metric approximation is used whose accuracy increases with rising signal-to-noise power ratio (SNR). Numerical results show that the system performance degradation in terms of the packet-error ratio (PER) due to erroneously detected modulation schemes is small in typical wireless communication scenarios if the proposed AMC algorithm is applied.

This paper investigates a novel efficient automatic modulation classification (AMC) algorithm in wireless time division duplex (TDD) orthogonal frequency division multiplexing (OFDM) systems with adaptive modulation (AM). Starting from the well-known maximum-likelihood (ML) classification metric for digital quadrature amplitude modulation (QAM) schemes, partial channel reciprocity in TDD-based adaptive OFDM systems is exploited by applying the maximum-a-posteriori (MAP) principle. A heuristic approach to find the required occurrence probabilities of the selected modulation schemes in a time-variant radio channel is analyzed. In order to reduce the overall computational complexity, a simplified metric is presented that is suitable for the implementation in real-time operating systems. Numerical results in terms of classification error probabilities confirm the effectivity of the complexity reductions. Moreover, it is demonstrated that the influence of the performance degradation on the overall packet error ratios (PER) due to imperfect modulation classification is small in a typical wireless communication scenario.

This paper presents a novel automatic modulation classification (AMC) algorithm in wireless time division duplex (TDD) orthogonal frequency division multiplexing (OFDM) systems with adaptive modulation (AM). Based upon the maximum-a-posteriori (MAP) criterion, a classification method for digital quadrature amplitude modulation (QAM) schemes is developed that exploits the channel reciprocity in TDD systems and the receiver knowledge about the overall transmission data rate. The AMC algorithm enables the application of adaptive modulation in wireless OFDM systems without loss in the effective data rate caused by signaling of the bit allocation table (BAT). Numerical results show almost no performance loss in terms of the packet error ratio (PER) compared to the case with perfect knowledge of the modulation schemes if the proposed AMC technique is used.

This paper introduces new joint estimation methods of multiple carrier frequency offsets (CFOs) and channel impulse responses (CIR) in application to multiuser orthogonal frequency division multiplexing (OFDM) systems. The estimators are derived from the optimal maximum-likelihood (ML) principle. Complexity reductions are achieved by exploiting the correlation properties of the training sequence (TS). The grid search algorithm is converted into a polynomial root finding procedure which leads to low-complexity closed-form estimators for moderate CFOs. Furthermore, iterative estimators for larger CFOs are proposed. Numerical results confirm that the performance degradation due to the approximations compared to the Cramer-Rao bound (CRB) is small and may be negligible in practice.

This contribution considers the estimation and compensation of residual multiple carrier frequency offsets (CFOs) and sampling frequency offsets (SFOs) in the uplink of multiuser OFDM systems. Based on the observation of a user-dependent phase rotation, an approximate maximum-likelihood (ML) estimator is proposed in order to estimate the CFOs and SFOs and to track the phase in a post-synchronization step. By using a Taylor approximation, the estimation complexity is reduced. Bit error-ratio (BER) simulations show that the system performance degradation due to (small) frequency mismatches is negligible if the proposed method is applied.

This contribution proposes a new joint estimation method of multiple carrier frequency offsets (CFOs) and channels in the uplink of multiuser orthogonal frequency division multiplexing (OFDM) systems. The estimators are derived from the optimal maximum-likelihood (ML) principle. Complexity reductions are achieved by exploiting the correlation properties of the training-sequence. The grid search algorithm is converted into a polynomial root finding procedure which leads to closed-form estimators for moderate CFOs. Numerical results confirm that the performance degradation due the approximations compared to the Cramer-Rao bound (CRB) is small and may be negligible in practice

This contribution considers the estimation and compensation of residual multiple carrier frequency offsets (CFOs) and sampling frequency offsets (SFOs) in the uplink of a multiuser OFDM system. Based on the observation of a user-dependent phase rotation, an approximate maximum-likelihood (ML) estimator is proposed in order to estimate the CFOs and SFOs to track the phase in a post-synchronization step. Bit-error-ratio (BER) simulations show that the system performance degradation due to (small) frequency mismatches is negligible if the proposed method is applied

We propose a new uplink to downlink spatial covariance matrix transformation technique that provides robustness against uplink steering vector mismatches for frequency duplex systems (FDD) in a frequency selective fading environment. Our approach leads to a matrix robust least squares (RLS) problem that can be formulated in a convex form as a second order cone (SOC) program and solved efficiently using the well-established interior point methods. The performance of the proposed method is compared with that of (K. Hugl et al., 1999) and (T. Aste et al., 1998) using a dynamic system level simulator developed for investigating the capacity improvement of a UMTS-FDD system with smart antennas at the base stations. Downlink capacity is used as a measure of system performance by finding the number of users for which a certain quality of service (QoS) requirement is fulfilled. System level simulations are carried out for 4-element uniform circular antenna (UCA) array. Simulation results show that our method outperforms both (K. Hugl et al., 1999) and (T. Aste et al., 1998) by supporting about 48% and 15% more average number of users than that of (K. Hugl et al., 1999) and (T. Aste et al., 1998) , respectively without any significant degradation in performance when compared to (T. Aste et al., 1998) with known uplink steering vector.

This paper presents a comparison of two different uplink to downlink covariance matrix transformation methods that can be used in downlink beamforming for frequency selective FDD channels. The performance improvement obtained from the transformation methods is compared with that from the method, which directly uses the uplink covariance matrix for downlink beamforming. For this purpose, a dynamic system level simulator developed for investigating the capacity improvement of a UMTS-FDD system employing smart antennas at the base stations is used. Downlink capacity is used as a measure of the performance of the system by finding the number of users for which a certain quality of service (QoS) requirement is fulfilled. System level simulations are carried out for antenna arrays with 2, 4 and 8 elements. Simulation results show that the downlink capacity can be improved significantly with higher number of antenna elements only if a transformation method is used in order to overcome the effect of different uplink and downlink carrier frequencies.

A new dynamic system level simulator is developed for investigating the capacity improvement in a UMTS-FDD system employing smart antennas at the base stations. The simulator is used to analyze the performance of this system by finding the number of users for which a certain quality of service (QoS) requirement is fulfilled. In this paper, analyses of the downlink capacity for different numbers of array elements with different adaptive beamforming strategies are presented. When using smart antennas, the real amount of performance improvement can only be estimated by system level simulations. Hence, dynamic system level simulations for UMTS-FDD are carried out which take into account the statistical nature of almost all parameters, e.g. wave propagation, user motion and traffic scenarios. Simulation results show a significant system capacity increase when beamforming is utilized.