In 3GPP New Radio (NR) Vehicle-to-Everything (V2X), the new standard for next-generation vehicular networks, vehicles can autonomously select sidelink resources for data transmission, which permits network operations without cellular coverage. However, standalone resource allocation is uncoordinated, and is complicated by the high mobility of the nodes that may introduce unforeseen channel collisions (e.g., when a transmitting vehicle changes path) or free up resources (e.g., when a vehicle moves outside of the communication area). Moreover, unscheduled resource allocation is prone to the hidden node and exposed node problems, which are particularly critical considering directional transmissions. In this paper, we implement and demonstrate a new channel access scheme for NR V2X in Frequency Range 2 (FR2), i.e., at millimeter wave (mmWave) frequencies, based on directional and beamformed transmissions along with Sidelink Control Information (SCI) to select resources for transmission. We prove via simulation that this approach can reduce the probability of collision for resource allocation, compared to a baseline solution that does not configure SCI transmissions.

Trajectory prediction is fundamental in computer Vision and autonomous driving, particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data, neglecting the challenges associated with out-of-view objects and the noise in-herent in sensor data due to limited camera range, physical obstructions, and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns, as they can result in missing essential, non-visible objects. To bridge this gap, we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects, our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj), setting a new benchmark for future research.

In this paper, we address the complex task of estimating the parameters for multiple propagation paths of realistic millimeter wave (mmWave) channels. We propose a solution with a reasonable computational complexity while providing high accuracy, which is required for precise positioning in joint localization and communication systems. We introduce an innovative method termed ESPRIT-D - beamspace Estimation of Signal Parameters via Rotational Invariance Techniques with a Dictionary based solution. It exploits a model for the mmWave multipath channel accounting for filtering effects, represented as a 5D tensor. Our solution develops a modification of beamspace ESPRIT that can operate with analog beamforming to extract the directions of departure and arrival in azimuth and elevation, while retrieving the delay estimates by a greedy sparse recovery method. We evaluated the proposed method using channel realizations generated by ray-tracing simulation of an outdoor environment, demonstrating an average angular error below 0.01° for line-of-sight (LoS) paths and 0.1° for nonline-of-sight (NLoS) components. The accuracy in delay estimation achieves an average of$3\mathrm{e}^{-10}\mathrm{s}$. Compared with state-of-the-art (SOTA), our algorithm exhibits a 10× improvement in estimation accuracy.

Neural network (NN)-based receivers for orthogonal frequency division multiplex (OFDM) excel through promising performance and benefits in their applicability. In this paper we analyze their capabilities when they are imposed with practical constraints that have to be considered when implementing such a receiver on hardware. Specifically, we focus on the effects of uniform linear affine quantization and it is shown which performance can be achieved by utilizing quantization-aware training (QAT) with trainable quantizers. In order to reduce the computational complexity of the NN-based receiver different pruning methods are investigated. We showcase that a reduction of the number of floating-point operations (FLOPs) by more than 50% is possible at the cost of less than 0.25 dB difference. Finally, an intuition on joint pruning and quantization is given.

Vehicle to vehicle communication is a part of essential use cases to further improve the vehicular traffic safety and efficiency. The 3rd Generation Partnership Project (3GPP) Rel-16 5G New Radio (NR) sidelink (SL) supports up to 31 blind Hybrid Automatic Repeat reQuest (HARQ) retransmissions for the SL broadcast communication with one of the goals to improve the communication reliability. We simulate a 6 lane highway scenario for low, medium and high traffic density to evaluate the effects of number of HARQ retransmissions on information age (IA). The results indicate a non-linear relationship between number of HARQ retransmissions and the improvement of IA, thus contributing to next stage of selection of appropriate number of HARQ retransmissions. We also analyse the complexities in the existing framework due to various network and system configurations according to Rel-16 standards.

Connected Automated Vehicles (CAVs) will use multiple V2X services to support connected and automated driving functions. The bandwidth required to support such services will augment as CAVs are gradually deployed. It is therefore important to accurately estimate the spectrum requirements to anticipate possible scalability challenges ahead. Current estimations consider a simplified modeling of the transmitter as well as context factors such as the number of vehicles in the communication range. Moreover, they do not accurately model if the Quality of Service (QoS) of the considered V2X services is satisfied or not. This study progresses the state of the art with a novel analytical model that quantifies the bandwidth required to support multiple V2X services. The model considers the impact of the vehicular context, the transmission parameters and the communication requirements to take into account the QoS at the receiver. This is important since adapting the transmission parameters can reduce the channel load but also impacts the probability to correctly receive each packet and therefore the bandwidth required to guarantee a target QoS at the receiver. The proposed model can be adapted to different wireless technologies and messages, but is applied in this study to quantify the bandwidth required by LTE-V2X to support the transmission of CAMs, CPMs and MCMs. The study demonstrates the scalability challenges ahead to support multiple V2X services.

Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. Nevertheless, real-world situations often involve obstructed cameras, missed objects, or objects out of sight due to environmental factors, leading to incomplete or noisy trajectories. To overcome these limitations, we propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories. LTrajDiff utilizes sensor data from mobile phones to surmount out-of-sight constraints, albeit introducing new challenges such as modality fusion, noisy data, and the absence of spatial layout and object size information. We employ a denoising diffusion model to predict precise layout sequences from noisy mobile data using a coarse-to-fine diffusion strategy, incorporating the Random Mask Strategy, Siamese Masked Encoding Module, and Modality Fusion Module. Our model predicts layout sequences by implicitly inferring object size and projection status from a single reference timestamp or significantly obstructed sequences. Achieving state-of-the-art results in randomly obstructed experiments, our model outperforms other baselines in extremely short input experiments, illustrating the effectiveness of leveraging noisy mobile data for layout sequence prediction. In summary, our approach offers a promising solution to the challenges faced by layout sequence and trajectory prediction models in real-world settings, paving the way for utilizing sensor data from mobile phones to accurately predict pedestrian bounding box trajectories. To the best of our knowledge, this is the first work that addresses severely obstructed and extremely short layout sequences by combining vision with noisy mobile modality, making it the pioneering work in the field of layout sequence trajectory prediction.

Cellular networks usually support non-safety-critical V2X services using Vehicle-to-Network (V2N) connections. However, the flexibility and capabilities of 5G have triggered interest in analyzing whether 5G could also support advanced V2X services using Vehicle-to-Network-to-Vehicle (V2N2V) connections instead of direct Vehicle-to-Vehicle (V2V) connections. V2N2V requires the integration of the 5G network with computing platforms for processing the V2X packets. The flexibility introduced by 5G facilitates the integration with multiple computing platforms such as Multi-access Edge Computing (MEC), edge cloud, shared data center or central cloud. This results in alternative 5G network deployments with the computing platform installed at different locations between the base station and the Internet. These deployments can have important technical implications for supporting V2X services. In this study, we analyze the impact of different 5G and computing platform deployments on the end-to-end (E2E) latency of V2N2V communications under multi-MNO (Mobile Network Operator) scenarios since vehicles may be served by different operators. We also identify which deployment strategies are more suitable to meet the latency requirements of V2X services for connected and automated driving.

5G and multi-access edge computing (MEC) are being considered to support V2X services demanding low latency and highly reliable communications using V2N2V (Vehicle-to-Network-to-Vehicles) communications instead of direct or sidelink V2V (Vehicle-to-Vehicle). Guaranteeing V2X service continuity using V2N2V is a challenging task in multi-Mobile Network Operator (MNO) deployments where vehicles are supported by different MNOs. MEC federations have been proposed to address some of these challenges. A MEC federation is a federated model of MEC systems enabling shared usage of MEC services and applications. Through MEC federations, vehicles can seamlessly access V2X applications independently of whether they are hosted on their MNO’s MEC, or on the MEC of a different (but federated) MNO. This paper presents the first study that analyses the impact of MEC federation on the end-to-end (E2E) latency when supporting V2X services using 5G V2N2V in multi-MNO scenarios. The paper also evaluates the feasibility to support the latency requirements of advanced V2X services in these scenarios, and the benefits introduced by MEC federation. This study considers the V2Xbased cooperative lane merge service as a case study.

An intelligent transportation system aims to provide various traffic safety and navigation services, and mainly relies on local perception and vehicular communication technologies. However, the vehicular communication technologies can be a target of wide range of attacks including position falsification, Sybil and denial-of-service (DoS) attacks which can lead to disastrous traffic accidents and jams. As a viable solution, misbehavior detection systems can be used in vehicular networks. Different from other works, in this paper, we propose a misbehavior detection system that utilizes both local perception and basic safety messages (BSM). Our work shows the methodology for generating realistic vehicular network data sets that include both local perception and BSM. In addition, we compare and show that the propose scheme is better compared to the previous scheme utilizing only beacon information for accurately identifying misbehavior in intelligent transportation system.

The high overhead of the beam training process is the main challenge when establishing mmWave communication links, especially for vehicle-to-everything (V2X) scenarios where the channels are highly dynamic. In this paper, we obtain prior information to speed up the beam training process by implementing two deep neural networks (DNNs) that realize radar-to-communication (R2C) channel information translation in a vehicle-to-infrastructure (V2I) system. Specifically, the first DNN is built to extract the information from the radar azimuth power spectrum (APS) to reconstruct the communication APS, while the second DNN exploits the information extracted from the spatial covariance of the radar channel to realize R2C covariance prediction. The achieved data rate and the similarity between the estimated and the true communication APS are used to evaluate the prediction performance. The covariance estimation method generally provides higher similarity, as the APS predictions cannot always capture the mismatch between the radar and communication APS. Compared to the beam training method which exploits directly the radar APS without an attempt to translate it to the communication channel, our proposed deep learning (DL) aided methods remarkably reduce the beam training overhead, resulting in a 13.3% and 21.9% rate increase when using the communication APS prediction and covariance prediction, respectively.

Tracking wideband millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems based on a hybrid architecture is a challenging problem, especially in high mobility scenarios where links are likely to be blocked by obstacles, such as trees, pedestrians or vehicles. In this paper, we propose a new strategy to track the frequency selective mmWave channel under blockage. We first introduce a statistical channel model that includes the evolution models for channel gains and angles of arrival and departure, as well as the statistics of blockage events. Then, we define a change point detection (CPD) test to identify the time instants where blockage appears/disappears, so the appropriate channel evolution models can be used for wideband channel tracking during the blockage events. To simultaneously achieve a high CPD success rate and a high accuracy in the channel estimate, we further propose a double digital combiner architecture that employs different digital combiners for CPD and channel tracking. Finally, we integrate into our framework a previously proposed Bayesian channel tracking algorithm. Simulation results show that the proposed approach achieves a good channel tracking performance even in mobile scenarios that suffer from highly dynamic blockage events.

We compare the potential of neural network (NN)-based channel estimation with classical linear minimum mean square error (LMMSE)-based estimators, also known as Wiener filtering. For this, we propose a low-complexity recurrent neural network (RNN)-based estimator that allows channel equalization of a sequence of channel observations based on independent time- and frequency-domain long short-term memory (LSTM) cells. Motivated by Vehicle-to-Everything (V2X) applications, we simulate time- and frequency-selective channels with orthogonal frequency division multiplex (OFDM) and extend our channel models in such a way that a continuous degradation from line-of-sight (LoS) to non-line-of-sight (NLoS) conditions can be emulated. It turns out that the NN-based system cannot just compete with the LMMSE equalizer, but it also can be trained w.r.t. resilience against system parameter mismatch. We thereby showcase the conceptual simplicity of such a data-driven system design, as this not only enables more robustness against, e.g., signal-to-noise-ratio (SNR) or Doppler spread estimation mismatches, but also allows to use the same equalizer over a wider range of input parameters without the need of re-building (or re-estimating) the filter coefficients. Particular attention has been paid to ensure compatibility with the existing IEEE 802.11p piloting scheme for V2X communications. Finally, feeding the payload data symbols as additional equalizer input unleashes further performance gains. We show significant gains over the conventional LMMSE equalization for highly dynamic channel conditions if such a data-augmented equalization scheme is used.

5G offers high flexibility at the radio, transport and core networks to support various services of critical verticals such as connected and automated driving. At the Radio Access Network (RAN), 5G defines a New Radio (NR). 5G NR utilizes different subcarrier spacing, slot durations, modulations and channel coding schemes. This flexibility offers the possibility to support automotive services with different and demanding requirements, such as Advanced Driver-Assistance System (ADAS), cooperative driving, and remote driving. Previous studies showed that 5G NR can be configured to achieve latencies below 2 ms. However, existing studies are generally restricted to scenarios with a limited number of users and unlimited bandwidth. Therefore, it is important to analyze whether 5G NR can effectively support these services as the network scales under limited spectrum allocations. This study advances the current state of the art to demonstrate that the capability of 5G NR RAN to support advanced V2X services depends on the RAN configuration (subcarrier spacing, slot duration and error protection) and network load.

A major source of difficulty when operating with large arrays at millimeter wave (mmWave) frequencies is to estimate the wideband channel, since the use of hybrid architectures acts as a compression stage for the received signal. Moreover, the channel has to be tracked and the antenna arrays regularly reconfigured to obtain appropriate beamforming gains when a mobile setting is considered. In this paper, we focus on the problem of channel tracking for frequency-selective mmWave channels, and propose two novel channel tracking algorithms. One of them exploits the sparsity of the mmWave channel, while the other one leverages prior statistical information about the channel parameters. We also propose a hybrid precoder and combiner design method to increase the received signal-to-noise ratio (SNR) during channel tracking, such that near-optimum data rates can be obtained with low-overhead. In our numerical results, we analyze the performance of our proposed algorithms for different system parameters. Simulation results show that our proposed channel tracking algorithms are able to achieve near-optimum data rates outperforming state-of-the-art methods.

A major challenge in millimeter wave (mmWave) communications is to configure the antenna arrays in the transceivers. The mobility of the users in mmWave cellular networks makes it necessary to periodically reconfigure the precoders and combiners, according to the variations of the channel. In this paper, we propose a new strategy to track the channel variations in a multi-user (MU) hybrid mmWave MIMO communication cellular network. Besides exploiting the sparse nature of the mmWave channel, this approach also leverages statistical knowledge of the channel parameters, which enables further overhead reductions. Simulation results show that using the proposed algorithm, it is possible to maintain an effective high data rate even in high mobility scenarios.

This paper proposes a framework to explore the optimization of applications where a distributed set of nodes/sensors, e.g., automated vehicles, collaboratively exchange information over a network to achieve real-time situational-awareness. To that end we propose a reasonable proxy for the usefulness of possibly delayed sensor updates and their sensitivity to the network resources devoted to such exchanges. This enables us to study the joint optimization of (1) the application-level update rates, i.e., how often and when sensors update other nodes, and (2), the transmission resources allocated to, and resulting delays associated with, exchanging updates. We first consider a network scenario where nodes share a single resource, e.g., an ad hoc wireless setting where a cluster of nodes, e.g., platoon of vehicles, share information by broadcasting on a single collision domain. In this setting we provide an explicit solution characterizing the interplay between network congestion and situational awareness amongst heterogeneous nodes. We then extend this to a setting where such clusters can also exchange information via a base station. In this setting we characterize the optimal solution and develop a natural distributed algorithm based on exchanging congestion prices associated with sensor nodes' update rates and associated network transmission rates. Preliminary numerical evaluation provides initial insights on the trade-offs associated with optimizing situational awareness and the proposed algorithm's convergence.

By providing information about the objects that are non-line of sight and/or beyond the detection range of the local sensors, inter-vehicle communication compensates for the limitations of vehicle tracking subsystem in automated driving systems that relies on on-board sensing devices. Tracking capability in such systems can further be improved by making optimal use of the communication channel through sharing of locally created map data instead of transmitting only beacon messages. Message length adaptation, together with transmit rate control can address the scalability issue inherent in the vehicular network. The content of the exchanged information is another important aspect that has significant impact on the map accuracy in cooperative driving systems. In this paper, we study different congestion and content control schemes for a communication architecture aimed at map sharing, and evaluate their performance in terms of a situational awareness metric, namely position tracking error. This paper determines that message content should be concentrated on mapped objects that are located farther away from the sender, but near the edge of local sensor range. This paper also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system.

Reliable and timely delivery of periodic V2V (vehicle-to-vehicle) broadcast messages is essential for realizing the benefits of connected vehicles. Existing MAC protocols for ad hoc networks fall short of meeting these requirements. In this paper, we present, CoReCast, the first collision embracing protocol for vehicular networks. CoReCast provides high reliability and low delay by leveraging two unique opportunities: no strict constraint on energy consumption, and availability of GPS clocks to achieve near-perfect time and frequency synchronization.

To enable situational awareness for automated driving in intelligent transportation systems (ITS), it is envisioned that vehicles will be equipped with sensors, and possibly perform collaborative sensing amongst themselves. Unfortunately such sensing is subject to obstructions, e.g., other vehicles, and the performance can be poor when the penetration of collaborating vehicles is low. A possible solution is to deploy sensing and communication capable infrastructure, e.g., road side units (RSUs) and base stations (BSs), to assist collaborative sensing. This paper explores the performance of infrastructure assisted sensing of roads under various deployment schemes. Our analytical results show that deploying RSUs at intersections and at even spacings is most efficient in covering the roads while cellular based sensors may subject to building obstructions and should be located along roads working as RSUs. RSUs located above the vehicles can have 100% coverage of vehicles once the communication range is large enough to reach relevant sensors. Infrastructure provides a second advantage in providing a dynamic view of the road and thus better coverage over time. Such benefit from sensing temporal diversity is shared by vehicles moving in the opposite direction, yet collaborating with such vehicles involves more challenging V2V communication given the high relative speed and obstruction unless leveraging V2I relays.

In this paper, we develop a framework for using the dedicated short range communication (DSRC) waveform for the purposes of automotive radar. Our approach operates on the frequency domain channel estimates generated by the OFDM physical layer used in DSRC. We consider a two path channel model, with the first cluster corresponding to direct signal interference and the second cluster corresponding to the signal reflected from the target. The target ranging and direction of arrival information is encoded in the parameters of the reflected path. We estimate the parameters of the direct and reflected path using the least squares matching pursuit algorithm by exploiting their relative power difference. The performance of the algorithm is evaluated through numerical simulations assuming low power omnidirectional 5 dBi antennas, Swerling type 0 and type 3 target models, 10 MHz transmission bandwidth and different analog-to-digital quantization resolutions. Simulations results show submeter accuracy in location estimation for a significant range of target distances.

By providing information about the objects that are non-line of sight and/or beyond the detection range of the local sensors, inter-vehicle communication compensates for the limitations of vehicle tracking subsystem in automated driving systems that relies on on-board sensing devices. Tracking capability in such systems can further be improved by making optimal use of the communication channel through sharing of locally created map data instead of transmitting only beacon messages. While map data are collected from both sensor and communicated information, exchanging a subset of the local map requires some sort of content control scheme in place to ensure that useful information is being exchanged among vehicles that would enhance tracking accuracy. This paper investigates how information sharing based on proximity of mapped objects impact the position tracking capability. It also compares deterministic and probabilistic variants of the distance based content control approaches. Preliminary results show that exchanging information about objects located near the edge of sensor range increases the tracking performance. It is also shown that the probabilistic approaches have higher mapping accuracy than their deterministic counterparts.

Understanding the surrounding environment including both still and moving objects is crucial to the design and optimization of intelligent vehicles. Knowledge about the vehicle environment could facilitate reliable detection of moving objects, especially irregular events (e.g., pedestrians crossing the road, vehicles making sudden lane changes,) for the purpose of avoiding collisions. Inspired by the analogy between point cloud and video data, we propose to formulate a problem of reconstructing the vehicle environment (e.g., terrains and buildings) from a sequence of point cloud sets. Built upon existing point cloud registration tool such as iterated closest point (ICP), we have developed an expectation-maximization (EM)-ICP technique that can automatically mosaic multiple point cloud sets into a larger one characterizing the still environment surrounding the vehicle. Moreover, we propose to address the issue of irregularity detection from the extracted moving objects. Our experimental results have shown successful reconstruction of a variety of challenging vehicle environments (including rural and urban, road and intersection, etc.) and simultaneous tracking/segmentation of multiple moving objects.

Millimeter wave (mmWave) could be a key technology to support high data rate demands for automated vehicles. MmWave needs array gain for the best performance, but this requires correctly pointing the beam, known as beam alignment. Dynamic blockages make beam alignment challenging in the vehicular setting. This paper proposes to leverage a vehicle's position along with past beam measurements to rank desirable pointing directions that can reduce the required beam training to a small set of pointing directions. The ranking is conducted using a learning-to-rank approach, which is a popular machine learning method used in recommender systems. The learning uses a kernel based model, and a new metric for evaluating ranked lists of pointing directions tailored to beam alignment is proposed. The proposed method provides a scalable framework for exploiting context information.

Beam alignment is a source of overhead in mobile millimeter wave communication systems due to the need for frequent repointing. Beam switching architectures can reduce the amount of repointing required by leveraging position prediction. This paper presents an optimization of beam design in terms of rate. We consider a non-congested two-lane highway scenario where road side units are installed on lighting poles. Under this scenario, line-of-sight to the road side unit is very likely and vehicle speed does not vary much. We formulate and solve numerically using a gradient descent method for an optimal beam design to maximize the data rate for non-overlap beams. The result shows close performance to the equal coverage beam design. We study the effect of the overlap on the average rate and outage and compare the equal coverage with the equal beamwidth design. Numerical examples show that the equal coverage design can achieve up to 1.5× the rate of the equal beamwidth design confirming the importance of the choice of beam design.

This paper proposes a frequency separation type current control system for a grid-connected single-phase five-level inverter. The proposed control system realizes high tracking performance and high disturbance suppression performance. To obtain high tracking performance to the sinusoidal reference signal, a repetitive controller is applied to the current controller. The current controller with a repetitive controller is capable of tracking to fundamental frequency, without the steady-state error. The disturbance observer using a notch filter (notch type disturbance observer) is used to obtain the high disturbance suppression performance. Using the notch type disturbance observer, the frequency components except for the fundamental frequency are suppressed. The proposed control system is configured by combining the current controller with a repetitive controller and a PI controller, and adding a notch type disturbance observer. Numerical simulation results and experimental results confirm that the proposed control system effectively reduces the output current distortion. The THD of the output current is reduced by 15.5 % when using the proposed control system.

This paper describes a control method for the self-sustained operation of a photovoltaic (PV) generation system to compensate the output voltage distortion compensation. In order to improve the output voltage distortion, the control circuit consists of sinusoidal tracking control based on the internal model principle. Moreover, the control system is configured by combining the sinusoidal tracking control and a notch type disturbance observer. Thus, this configuration of the control system produces lower output voltage distortion. In addition, the zero location of sensitivity function is relocated by appropriately modifying the feedback element of the disturbance observer. The simulation and experimental results confirm that the proposed disturbance observer effectively reduces the output voltage distortion and improves the disturbance suppression characteristics. The THD of the output voltage is improved by 3.15 % using the proposed zero re-allocated disturbance observer in the case of the resistance and rectifier.

This paper investigates the application of interference alignment (IA) to inter-vehicle communications for further performance improvement. In conventional IA in the spatial domain, the number of antennas of each terminal limits the number of terminals, and therefore the sum rate performance is upper-bounded. This paper shows that when IA is applied to inter-vehicle communications at an intersection, it is possible to exceed the sum rate of the conventional IA by taking into account the effect of path loss. To this end, we consider multiple-input multiple-output interference channels with two IA groups, where all interference signals from the same group are aligned at each receiver by virtue of IA, but interference signals from the other group are not. Simulation results show that two IA groups can geographically co-exist by utilizing high propagation loss at a corner of the intersection to mitigate inter-group interference, and thus the achievable sum rate can be significantly improved, compared with that of single- group IA.

We consider secret key agreement based on radio propagation characteristics in a two-way relaying system where two legitimate parties named Alice and Bob communicate with each other via a trusted relay. In this system, Alice and Bob share secret keys generated from measured radio propagation characteristics with the help of the relay in the presence of an eavesdropper. We present four secret key agreement schemes: an amplify-and-forward (AF) scheme, a signal-combining amplify-and-forward (SC-AF) scheme, a multiple-access amplify-and-forward (MA-AF) scheme, and an amplify-and-forward with artificial noise (AF with AN) scheme. In these schemes, the basic idea is to share the effective fading coefficients between Alice and Bob and use them as the source of the secret keys. The AF scheme is based on a conventional amplify-and-forward two-way relaying method, whereas in the SC-AF scheme and the MA-AF scheme, we apply the idea of physical-layer network coding to the secret key agreement. In the AF with AN scheme, the relay transmits artificially generated noise, as well as channel information signal, in order to conceal the latter. Simulation results show that the MA-AF scheme outperforms the other schemes in Rayleigh fading channels, whereas the AF with AN scheme is suitable for Rician fading channels.

We consider secret key agreement based on radio propagation characteristics in two-way relaying system where two legitimate parties named Alice and Bob communicate with each other via a relay. In this system, Alice and Bob share secret keys generated from their radio propagation characteristics by the help of the relay. In this paper, we present two secret key agreement schemes: an amplify-and-forward (AF) scheme and a multiple-access amplify-and-forward (MA-AF) scheme. In both of the schemes, the basic idea is to share the effective fading coefficient between Alice and Bob and use it as the source of secret keys. The AF scheme is based on a conventional amplify-and-forward two-way relaying method, while the MA-AF scheme utilizes the inherent combining of signals provided by simultaneous transmissions over a multiple-access channel in order to share the secret keys more securely efficiently. We analyze eavesdropping strategy in terms of eavesdropper's location and show that if the eavesdropper is located near the relay and can receive signals from the relay without multipath fading and noise, the AF scheme is not secure. Simulation results show that the MA-AF scheme is more secure and efficient than the AF scheme.

We consider information reconciliation in secret key agreement from a fading channel where two legitimate parties utilize channel estimates of their fading channel as correlated random variables to generate a secret key. The information reconciliation is a process of correcting the discrepancies between the legitimate parties' keys by public discussion in the presence of eavesdropper, where the amount of the information revealed in the public discussion is needed to be as little as possible. In this paper, we propose information reconciliation protocols using the reliability values of channel estimates in order to correct errors effectively. The proposed information reconciliation protocols are modified versions of a protocol used in quantum key distribution called sliced error correction using Cascade. Simulation results show that the proposed protocols can correct errors with less the number of disclosed bits and less the number of communications than the conventional sliced error correction using Cascade.