This paper proposes a novel multi-party key generation method that jointly utilizes channel state information (CSI) and blockchain technology to enhance security in distributed systems. The proposed method starts by extracting CSI from wireless channels, leveraging the channels’ inherent randomness and reciprocity to generate secure key fragments shared among legitimate parties. Then, the key generation process involves several stages, including quantization, reconciliation, and privacy amplification, ensuring that the resulting keys are secure and synchronized across participants. Blockchain technology is then leveraged to securely commit these keys, ensuring that the key agreements are recorded in a decentralized, tamper-resistant ledger. The proposed method effectively combines the physical-layer properties of CSI with the decentralized nature of blockchain, providing robust protection against eavesdropping and tampering attacks. Theoretical analyses and simulation results demonstrate the effectiveness of the proposed method in terms of key mismatch probability and secrecy capacity. Additionally, the randomness of the generated keys by the proposed method is validated using the National Institute of Standards and Technology randomness tests.

Physical layer security (PLS) has garnered increasing attention as a complementary solution to conventional crypto-graphic techniques for addressing the diversity of use cases, deployment scenarios, and device capabilities in today’s wireless networks. Integrating artificial intelligence (AI) into PLS offers a promising solution to various multi-dimensional, heterogeneous, and complex challenges stemming from the growing complexity of networks. However, the opaque nature of AI has raised concerns regarding its trustworthiness and interpretability. This paper addresses these concerns by emphasizing the crucial role of explainability in AI-based PLS in several use cases of next-generation networks. A particular focus is placed on physical layer authentication through an illustrative case study, aiming to enhance AI-empowered PLS’s practicality and trustworthiness. The paper concludes with a discussion of some critical future research directions.

While being tracked by an uncooperative radar, calculating the distance to the radar becomes a vital subject for electronic warfare (EW) systems. In most scenarios, the calculation is required to be immediate so that the EW system can respond quickly and dodge a possible threat. In this study, we show that a single moving EW system can find its distance to an uncooperative tracking radar in the order of seconds. We develop two different approaches utilizing non-convex optimization and transformer-based image segmentation methods. The optimization methods work directly on the time of arrival measurements of the received pulses. Moreover, we propose a pre-processing method to produce two-dimensional input images of the segmentation models, in which the instantaneous distance to the radar appears as an empty corridor. The models aim to extract this corridor, ultimately revealing the distance information. Quantitative results show that both approaches can accurately estimate the distance to the radars.

This paper presents field measurement-based channel characterization for air–to–ground (A2G) and air–to– air (A2A) wireless communication systems using two drones equipped with lightweight software-defined radios. A correlation-based channel sounder is employed such that the transmitting drone broadcasts the sounding waveform with a pseudo-noise sequence and the receiving drone captures the sounding waveform together with the location information for the post-processing analysis. The path loss results demonstrate that the measurement and flat-earth two-ray results have similar trends for A2G while the measurement and free space path loss are similar to each other for A2A. The time delays between the direct path and multipath components are widely spread for A2A while the multipath components are mostly concentrated around the direct path for A2G generating a more challenging communication environment. We observe that the reflections from several buildings having metal roofs and claddings on the measurement site cause sudden peaks in the root-mean-square delay spread. The results indicate that the A2A channel has better characteristics than the A2G under similar mobility conditions.

This paper proposes a novel time-frequency warped waveform for short symbols, massive machine-type communication (mMTC), and internet of things (IoT) applications. The waveform is composed of asymmetric raised cosine (RC) pulses to increase the signal containment in time and frequency domains. The waveform has low power tails in the time domain, hence better performance in the presence of delay spread and time offsets. The time-axis warping unitary transform is applied to control the waveform occupancy in time-frequency space and to compensate for the usage of high roll-off factor pulses at the symbol edges. The paper explains a step-by-step analysis for determining the roll-off factors profile and the warping functions. Gains are presented over the conventional Zero-tail Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (ZT-DFT-s-OFDM), and Cyclic prefix (CP) DFT-s-OFDM schemes in the simulations section.

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components’ identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.

Unmanned aerial vehicles (UAVs) play a critical role in the fifth-generation and beyond communications. Since UAVs have an important potential in terms of getting deployed as base stations in next generation wireless networks, an extensive elaboration is required to model UAV wireless communication channels both in terms of large-scale and small-scale characteristics. To this end, the main objective of this study is to reveal alterations of air-to-ground wireless propagation channels for possible base station allocation at the height of 50 m, based on real measurements. The large-scale characteristics of the channel are analyzed by log-distance path loss model and the flat earth two-ray model. The small-scale characteristics of the propagation channel are examined considering the Rician K factor and root mean square delay spread. The measurement results also include channel impulse response and power delay profile results make it possible to observe the multi-path components on the wireless communication channel. The measurement campaign is conducted in a setting of hilly terrain layout at 2.57 GHz which is one of the 5G mid-band frequencies.

Powered by the advances in microelectronics technologies, unmanned aerial vehicles (UAVs) provide a vast variety of services ranging from surveillance to delivery in both military and civilian domains. It is clear that a successful operation in those services relies heavily on wireless communication technologies. Even though wireless communication techniques could be considered to reach a certain level of maturity, wireless communication links including UAVs should be regarded in a different way due to the peculiar characteristics of UAVs such as agility in 3D spatial domain and versatility in modes of operation. Such mobility characteristics in a vast variety of environmental diversity render links including UAVs different from those in traditional, terrestrial mobility scenarios. Furthermore, UAVs are critical instruments for network operators in order to provide basic voice and short messaging services for narrow band communication in and around disaster areas. It is obvious that such widespread use of UAVs under different scenarios and environments requires a better understanding the behavior of the communication links that include UAVs. Therefore, in this study, details of a measurement campaign designed to collect data for large-scale propagation characterization of air-to-ground links operated by UAVs at 446MHz under narrowband assumption are given. Data collection, post-processing, and measurement results are provided.

The applications of the unmanned aerial vehicles (UAVs) increase rapidly in everyday life, thus detecting the UAVs and/or its pilot is a crucial task. Many UAVs adopt frequency hopping spread spectrum (FHSS) technology to efficiently and securely communicate with their radio controllers (RCs) where the signal follows a hopping pattern to prevent harmful interference. In order to realistically distinguish the frequency hopping (FH) RC signals, one should consider the real-world radio propagation environment since many UAVs communicate with RCs from a far distance in which signal faces both slow and fast fading phenomenons. Therefore, in this study different from the literature, we consider a system that works under real- conditions by capturing over-the-air signals at hilly terrain suburban environments in the presence of foliages. We adopt the short-time Fourier transform (STFT) approach to capture the hopping sequence of each signal. Furthermore, time guards associated with each hopping sequence are calculated using the autocorrelation function (ACF) of the STFT which results in differentiating the each UAV RC signal accurately. In order to validate the performance of the proposed method, the results of normalized mean square error (MSE) respect to different signal-to-noise ratio (SNR), window size and Tx-Rx separation values are given.

In this paper, a deep convolutional neural network-based symbol detection and demodulation is proposed for generalized frequency division multiplexing with index modulation (GFDM-IM) scheme in order to improve the error performance of the system. The proposed method first pre-processes the received signal by using a zeroforcing (ZF) detector and then uses a neural network consisting of a convolutional neural network (CNN) followed by a fully-connected neural network (FCNN). The FCNN part uses only two fully-connected layers, which can be adapted to yield a trade-off between complexity and bit error rate (BER) performance. This two-stage approach prevents the getting stuck of neural network in a saddle point and enables IM blocks processing independently. It has been demonstrated that the proposed deep convolutional neural network-based detection and demodulation scheme provides better BER performance compared to ZF detector with a reasonable complexity increase. We conclude that non-orthogonal waveforms combined with IM schemes with the help of deep learning is a promising physical layer (PHY) scheme for future wireless networks.

In this letter, generalized frequency division multiplexing (GFDM) is combined with index modulation (IM) exploiting the block-based structure of GFDM in an innovative way. The proposed GFDM-IM scheme uses flexible IM numerology to reduce out-of-band (OOB) emission and provides a novel multilayer system model to further benefit from the enhanced distance spectrum of IM. The proposed scheme adapts maximum likelihood successive interference cancellation based near-optimum detector to improve the error performance and reduce the computational complexity. It has been shown that the proposed GFDM-IM scheme achieves better error performance than classical and IM-based orthogonal frequency division multiplexing and GFDM schemes at the same spectral efficiency, and provides an interesting tradeoff among OOB emission, spectral efficiency, and latency.

This paper explores the performance of time-domain synchronous orthogonal frequency-division multiplexing (TDS-OFDM) systems operated under rapidly time-varying mobile channels. Since a rapidly time-varying channel contains more unknown channel coefficients than the number of observations, the mobile channel can conveniently be modeled with the discrete Legendre polynomial basis expansion model to reduce the number of unknowns. The linear minimum mean square error (LMMSE) estimate can be exploited for channel estimation on inter-block-interference-free received signal samples owing to transmitting pseudo-noise (PN) sequences. In conventional TDS-OFDM systems, the channel estimation performance is limited due to estimating channel responses only from the beginning part of the channel. Therefore, a new system model named “partitioned TDS-OFDM system” is proposed to improve the system performance by inserting multiple PN sequences to the middle and end parts of the channel as well. In addition to providing the reconstruction error performance, Bayesian Cramer-Rao lower bound is derived analytically. Also, the LMMSE-based symbol detection is employed. To alleviate the negative effects of inter-carrier-interference (ICI) occuring in mobile channels, ICI cancellation is applied to enhance the detection performance. The simulation results demonstrate that the proposed TDS-OFDM system is superior to the conventional system and its corresponding performance is able to approach the achievable lower performance bound.

In compressive sensing (CS)‐based spectrum sensing literature, most studies consider accurate reconstruction of the primary user signal rather than detection of the signal. Furthermore, possible absence of the signal is not taken into account while evaluating the spectrum sensing performance. In this study, Bayesian CS is studied in detail for primary user detection. In addition to assessing the signal reconstruction performance and comparing it with the conventional basis pursuit approach and the corresponding lower bounds, signal detection performance is also considered both analytically and through simulation studies. In the absence of a primary user signal, the trade‐off between probabilities of detection and false alarm is studied as it is equally important to determine the performance of a CS approach when there is no active primary user. To reduce the computation time and yet achieve a similar detection performance, finally the effect of number of iterations is studied for various systems parameters including signal‐to‐noise‐ratio, compression ratio, mean value of accumulated energy and threshold values. The presented framework in this study is important in the overall implementation of CS‐based approaches for primary user detection in practical realisations such as LTE downlink OFDMA as it considers both signal reconstruction and detection.

Blind and noncooperative identification of the transmission parameters of unknown communication signals has been employed both in military and civilian applications. Multiple-Input-Multiple-Output (MIMO) transmission systems emerging in the last decade pose new challenges to the signal identification systems, one of which is the identification of the Space-Time Block Code (STBC) used in the transmission. In this work, we present a novel STBC classification algorithm that exploits the joint wide sense cyclostationary characteristics of the coded transmit signals as discriminating features. Compared to existing algorithms, the proposed method can discriminate between a large number of different STBCs.

Along with the widespread usage of mobile devices supporting multiple radio interfaces, users can connect to the mobile operator's network using different access technologies such as UMTS, WLAN and WiMAX/LTE. Depending on the network conditions and the QoS requirements of users, the usage of the right technology at the right time can provide more effective networking with higher data rate, lower cost, longer battery duration, and higher coverage area. To enable this networking effectiveness, a seamless handover mechanism to minimize service disruption is required. IEEE 802.21, which defines a set of standards for media independent handover, is capable of realizing both horizontal and vertical handovers. In this paper, a new algorithm is proposed to provide energy efficient handovers among UMTS, WiMAX, and WLAN networks. The proposed algorithm is implemented within the IEEE 802.21 framework and its performance analysis is performed using the ns-2.29 simulator. Simulation results show that the proposed algorithm significantly increases the service duration and hence decreases packet losses by dynamically changing the video downloading rate according to handover decisions and the remaining battery power levels.

For a single carrier frequency division multiple access (SC-FDMA) system, we obtain the jointly optimal power and chunk allocation policies which maximize the sum rate. Our solution is applicable to both localized and interleaved subcarrier mapping schemes. We solve the joint optimization problem by sequentially solving two sub-problems: power allocation and chunk allocation. Primarily, we use an optimal power allocation algorithm, which we derive from Karush-Kuhn-Tucker (KKT) conditions; and then we convert the optimum chunk assignment problem into a maximum weighted matching problem on a bipartite graph, and hence solve it in polynomial time. We also propose two greedy chunk allocation algorithms with lower complexity, and demonstrate that these algorithms produce near optimal results, especially for interleaved subcarrier mapping, when used in conjunction with optimal power control.

This work proposes a novel iterative blind receiver to jointly estimate channel parameters and transmitted data sequence on the multipath fading channel in Direct Sequence Code Division Multiple Access (DS CDMA) systems. Since time delay of the transmission channel represents nonlinear characteristic, joint estimation of these parameters and data sequence is difficult for multipath fading channels. To derive of the proposed iterative method we used completely Bayesian approach and then, Sequential Monte Carlo (SMC) technique is adapted to proposed method.

Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a promising technique for reaching high data rates targeted in the 3rd Generation Partnership Project - Long Term Evolution (3GPP-LTE). MIMO-OFDM channel estimation schemes play a very important role on achieving this aim. However, the complexity of the estimators increases exponentially due to the structure of the MIMO systems. This causes an increase in the computational burden of the transceivers. As a result, the complexity of the channel estimators is becoming an important issue in real world MIMO-OFDM applications. In this paper, the performance of the low complexity Minimum Mean Square Error (MMSE) channel estimator scheme based on Karhunen-Loéve (KL) series expansion coefficients for the 3GPP-LTE Downlink MIMO-OFDM systems is examined. System level simulations are accomplished to compare the performances of the estimators under the spatially correlated channel coefficient variations.

Adaptive modulation is an important method for effective usage of channel capacity in Orthogonal Frequency Division Multiplexing (OFDM) systems. Modulation identification algorithms unnecessitate the need of transferring the modulation information to the receiver, thereby these algorithms are very effective methods to increase the channel capacity. However, in practice, separate two sets of pilot symbols are used to identify the modulation type and to estimate the channel impulse response. In this paper, we propose one set of pilot symbols to get information about the channel and the modulation type, jointly. These pilots are named as “adaptive pilots” because they are related with the modulation type. Monte Carlo simulations are performed to observe the performance of the proposed adaptive pilot method and to compare with previously presented algorithm given in [1]. Simulation results indicate that the proposed adaptive pilots successfully help to identify correctly the modulation type without affecting the performance of the channel estimation.

Cognitive radio is an enabling technology for efficient utilization of radio spectrum. In this paper, an orthogonal frequency division multiple access (OFDMA) based cognitive (opportunistic) network is considered, which co-exists with a macrocell network through utilizing its unused subcarriers. We particularly consider the uplink opportunity detection problem by the opportunistic network, where accurate synchronization to the macrocell network is crucial for minimizing the interference received from the macrocell users. After demonstrating the impact of synchronization on the inter-carrier-interference (ICI) observed at the opportunistic network, an improved blind first-user synchronization technique is proposed, and its statistics are analyzed. Through computer simulations it is shown that the proposed technique yields less interference and better opportunities for the opportunistic network.

Long term evolution (LTE) systems are expected to use single carrier frequency division multiple access (SC-FDMA) for the uplink. Being very similar to the OFDMA technology, SC-FDMA is sensitive to frequency offsets, which leads to inter-carrier interference (ICI). In this paper, we propose an interpolation algorithm based on adaptive order polynomial fitting for LTE uplink channel estimation to mitigate ICI in high Doppler spread. Simulation results show that the proposed method has better performance compared to the conventional schemes.

The challenging problem in the design of digital receivers of today's and future high-speed, high data-rate wireless communication systems is to implement the optimal decoding and channel estimation processes jointly in a computationally feasible way. Without realising such a critical function perfectly at receiver, the whole system will not work properly within the desired performance limits. Unfortunately, direct implementation of such optimal algorithms is not possible mainly due to their mathematically intractable and computationally prohibitive nature. A novel algorithm that reaches the performance of the optimal maximum a posteriori (MAP) algorithm with a feasible computational complexity is proposed. The algorithm makes use of a powerful statistical signal processing tool called the expectation–maximisation (EM) technique. It iteratively executes the MAP joint channel estimation and decoding for space–time block-coded orthogonal frequency division multiplexing systems with turbo channel coding in the presence of unknown wireless dispersive channels. The main novelty of the work comes from the facts that the proposed algorithm estimates the channel in a non-data-aided fashion and therefore except a small number of pilot symbols required for initialisation, no training sequence is necessary. Also the approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen–Loeve (KL) orthogonal expansion and finds MAP estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MAP estimator. Moreover, optimal rank reduction is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the iterative estimation approach.

In this work, we propose a joint MAP channel estimation and data detection technique based on the expectation maximization (EM) method with parallel interference cancelation (PIC) for downlink multi-carrier (MC) code division multiple access (CDMA) systems in the presence of frequency selective channels. The EM algorithm derived estimates the complex channel parameters of each subcarriers iteratively and generates the soft information representing the data a posterior probabilities. The soft information is then employed in a PIC module to detect the symbols efficiently. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The performance of the proposed approach are studied in terms of bit-error rate (BER) and mean square error (MSE). Throughout the simulations, extensive comparisons with previous works in literature are performed, showing that the new scheme can offer superior performance.

We consider the design of turbo receiver structures for space-frequency block coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in the presence of unknown frequency and time selective fading channels. The Turbo receiver structures for SFBC-OFDM systems under consideration consists of an iterative MAP Expectation/Maximization (EM) channel estimation algorithm, soft MMSE-SFBC decoder and a soft MAP outer-channel-code decoder. MAP-EM employs iterative channel estimation and it improves receiver performance by re-estimating the channel after each decoder iteration. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The optimal truncation property of the KL expansion can reduce computational load on the iterative estimation approach. The performance of the proposed approaches are studied in terms of mean square error and bit-error rate. Through computer simulations, the effect of a pilot spacing on the channel estimator performance and sensitivity of turbo receiver structures on channel estimation error are studied. Simulation results illustrate that receivers with turbo coding are very sensitive to channel estimation errors compared to receivers with convolutional codes. Moreover, superiority of the turbo coded SFBC-OFDM systems over the turbo coded STBC-OFDMsystems is observed especially for high Doppler frequencies.

In this paper, based on a sequential Monte Carlo method, a computationally efficient algorithm is presented for blind data detection in the presence of residual phase noise generated at the output the phase tracking loop employed in a digital receiver. The basic idea is to treat the transmitted symbols as "missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise and the incoming data are obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology.

The paper proposes a computationally efficient, pilot-aided, minimum mean square error (MMSE), channel estimation algorithm for OFDM systems. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and estimates uncorrelated series expansion coefficients. Moreover, optimal rank reduction is achieved in the proposed approach by exploiting the optimal truncation property of the KL expansion, resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We first consider the stochastic Cramer-Rao bound and derive the closed-form expression for the random KL coefficients. We then exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE.

In this paper, maximum likelihood estimator is proposed for passive localization of narrowband sources in the spherical coordinates (azimuth, elevation, range). We adapt expectation/maximization iterative method to solve the complicated multi-parameter optimization problem appearing on the 3-D localization problem. The proposed algorithm is based on maximum likelihood criterion, which employs the source signals recorded by 2-D array under near-field assumption. Expectation/maximization algorithm decomposes the observed data into its components and then estimates the parameters of each signal component separately providing computationally efficient solution to the resulting optimization problem. Finally, some numerical simulations illustrate the applicability and effectiveness of the proposed algorithm.

In this paper, a computationally efficient, non-data-aided maximum a posteriori (MAP) channel estimation algorithm is proposed for orthogonal frequency division multiplexing (OFDM) systems with transmitter diversity using space-time block coding. The Alamouti's transmit diversity scheme with two transmit antennas is employed here and generalized for OFDM systems. The algorithm requires a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve orthogonal expansion and estimates the complex channel parameters of each subcarrier iteratively using the Expectation Maximization (EM) method, which converges to the true MAP estimation of the unknown channel. An analytical expression is derived for the Modified Cramer-Rao lower bound of the proposed MAP channel estimator. The performance is presented in terms of the mean-square error for a system employing QPS signaling.

Space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. We consider the problem of blind estimation of the channel parameters along with space-time coded signals. Our proposed approach exploits the finite alphabet property of the space-time coded signals and is based on the unconditional signal model by treating the information sequence as stochastic i.i.d. sequences. The iterative Baum-Welch (1970) algorithm is then adapted to solve the resulting unconditional ML optimization cost function. Finally, some simulation results are presented.

If error correcting coding is present in the information symbols, the channel estimation procedure may be further complicated, since the encoder introduces a nonlinear operation on the information symbols (in the field of reals). Moreover, because of the encoder's memory, the input to the channel may not be i.i.d. Therefore classical blind channel equalization methods may not be suitable for systems with coding. A blind stochastic maximum-likelihood channel estimation algorithm is proposed for convolutionally coded signals transmitted through a multipath channel. The performance of the estimator is explored, based on the evaluation of approximate Cramer-Rao bounds. The CRBs are used in turn to obtain approximate expressions for the probability of error. Finally, some illustrative simulations are presented.

A blind stochastic maximum likelihood (ML) channel estimation algorithm is adapted to incorporate a known training sequence as part of the transmitted frame. A hidden Markov model (HMM) formulation of the problem is introduced, and the Baum-Welch (1970) algorithm is modified to provide a computationally efficient solution to the resulting optimization problem. The proposed method provides a unified framework for semi-blind channel estimation, which exploits information from both the training and the blind part of the received data record. The performance of the ML estimator is studied, based on the evaluation of Cramer-Rao bounds (CRBs). Finally, some preliminary simulation results are presented.

Direct sequence code division multiple access (CDMA) systems are considered, in a multipath environment. We address the problem of estimating each user's signature waveform without requiring training sequences. We show that the problem is considerably simplified if a novel transmission strategy is adopted, which combines spreading and interleaving. In this setup, chip sequences corresponding to successive bits are interleaved before transmission. Novel channel estimation algorithms are developed in this chip interleaving framework and their performance is analyzed. Adaptive implementations are derived and some illustrative simulations are presented.