Automated medical image segmentation plays a crucial role in assisting doctors in diagnosing diseases. Feature decoding is a critical yet challenging issue for medical image segmentation. To address this issue, this work proposes a novel feature decoding network, called multi-scale group agent attention-based graph convolutional decoding networks (MSGAA-GCDN), to learn local-global features in graph structures for 2D medical image segmentation. The proposed MSGAA-GCDN combines graph convolutional network (GCN) and a lightweight multi-scale group agent attention (MSGAA) mechanism to represent features globally and locally within a graph structure. Moreover, in skip connections a simple yet efficient attention-based upsampling convolution fusion (AUCF) module is designed to enhance encoder-decoder feature fusion in both channel and spatial dimensions. Extensive experiments are conducted on three typical medical image segmentation tasks, namely Synapse abdominal multi-organs, Cardiac organs, and Polyp lesions. Experimental results demonstrate that the proposed MSGAA-GCDN outperforms the state-of-the-art methods, and the designed MSGAA is a lightweight yet effective attention architecture. The proposed MSGAA-GCDN can be easily taken as a plug-and-play decoder cascaded with other encoders for general medical image segmentation tasks.

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.

Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles. However, the absence of feature maps shared from other vehicles can lead to a significant decline in 3D object detection performance for cooperative perception models compared to standalone 3D detection models. This drawback impedes the adoption of cooperative perception as vehicle resources are often insufficient to concurrently employ two perception models. To tackle this issue, we present Simultaneous Individual and Cooperative Perception (SiCP), a generic framework that supports a wide range of the state-of-the-art standalone perception backbones and enhances them with a novel Dual-Perception Network (DP-Net) designed to facilitate both individual and cooperative perception. In addition to its lightweight nature with only 0.13M parameters, DP-Net is robust and retains crucial gradient information during feature map fusion. As demonstrated in a comprehensive evaluation on the V2V4Real and OPV2V datasets, thanks to DP-Net, SiCP surpasses state-of-the-art cooperative perception solutions while preserving the performance of standalone perception solutions. The source code can be found at https://github.com/DarrenQu/SiCP.

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.

Correspondence identification (CoID) is an essential component for collaborative perception in multi-robot systems, such as connected autonomous vehicles. The goal of CoID is to identify the correspondence of objects observed by multiple robots in their own field of view in order for robots to consistently refer to the same objects. CoID is challenging due to perceptual aliasing, object non-covisibility, and noisy sensing. In this paper, we introduce a novel deep masked graph matching approach to enable CoID and address the challenges. Our approach formulates CoID as a graph matching problem and we design a masked neural network to integrate the multimodal visual, spatial, and GPS information to perform CoID. In addition, we design a new technique to explicitly address object non-covisibility caused by occlusion and the vehicle's limited field of view. We evaluate our approach in a variety of street environments using a high-fidelity simulation that integrates the CARLA and SUMO simulators. The experimental results show that our approach outperforms the previous approaches and achieves state-of-the- art CoID performance in connected autonomous driving applications. Our work is available at: https://github.com/gaopeng5/DMGM.git.

Vehicular cooperative perception aims to provide connected and automated vehicles (CAVs) with a longer and wider sensing range, making perception less susceptible to occlusions. However, this prospect is dimmed by the imperfection of onboard localization sensors such as Global Navigation Satellite Systems (GNSS), which can cause errors in aligning over-the-air perception data (from a remote vehicle) with a Host vehicle’s (HV’s) local observation. To mitigate this challenge, we propose a novel LiDAR-based relative localization framework based on the iterative closest point (ICP) algorithm. The framework seeks to estimate the correct transformation matrix between a pair of CAVs’ coordinate systems, through exchanging and matching a limited yet carefully chosen set of point clouds and usage of a coarse 2D map. From the deployment perspective, this means our framework only consumes conservative bandwidth in data transmission and can run efficiently with limited resources. Extensive evaluations on both synthetic dataset (COMAP) and KITTI-360 show that our proposed framework achieves state-of-the-art (SOTA) performance in cooperative localization. Therefore, it can be integrated with any upper-stream data fusion algorithm and serves as a preprocessor for high-quality cooperative perception.

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.

Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.

In this paper, we present Vi-Fi, a multi-modal system that leverages a user's smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e.g. WiFi MAC address). Our approach uses a recurrent multi-modal deep neural network that exploits FTM and IMU measurements along with distance between user and camera (depth information) to learn affinity matrices. As a baseline method for comparison, we also present a traditional non deep learning approach that uses bipartite graph matching. To facilitate evaluation, we collected a multi-modal dataset that comprises camera videos with depth information (RGB-D), WiFi FTM and IMU measurements for multiple participants at diverse real-world settings. Using association accuracy as the key metric for evaluating the fidelity of Vi-Fi in associating human users on camera feed with their phone IDs, we show that Vi-Fi achieves between 81% (real-time) to 91% (offline) association accuracy.

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.

Cooperative perception provides a novel way to conquer the sensing limitation on a single automated vehicle and potentially improves driving safety. To reduce the transmission data volume, existing solutions use the intermediate data generated by convolutional neural network (CNN) models, namely, feature maps, to achieve cooperative perception. The feature maps are however too large to be transmitted by the current V2X technology. We propose a novel approach, called Slim-FCP, to significantly reduce the transmission data size. It enables a channelwise feature encoder to remove irrelevant features for a better compression ratio. In addition, it adopts an intelligent channel selection strategy through which only representative channels of feature maps are selected for transmission. To evaluate the effectiveness of Slim-FCP, we further define a recall-to-bandwidth (RB) ratio metric to quantitatively measure how the recall of object detection changes with respect to the available network bandwidth. Experiment results show that Slim-FCP reduces the transmission data size by 75%, compared with the best state-of-the-art solution, with a subtle loss on object detection’s recall.

Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles. To enable collaborative localization, several model-based state estimation and learning-based localization methods have been developed. Given their encouraging performance, model-based state estimation often lacks the ability to model the complex relationships among multiple objects, while learning-based methods are typically not able to fuse the observations from an arbitrary number of views and cannot well model uncertainty. In this paper, we introduce a novel spatiotemporal graph filter approach that integrates graph learning and model-based estimation to perform multi-view sensor fusion for collaborative object localization. Our approach models complex object relationships using a new spatiotemporal graph representation and fuses multi-view observations in a Bayesian fashion to improve location estimation under uncertainty. We evaluate our approach in the applications of connected autonomous driving and multiple pedestrian localization. Experimental results show that our approach outperforms previous techniques and achieves the state-of-the-art performance on collaborative localization.

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.

We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multi-modal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network.

A comprehensive vehicular network analysis requires modeling the street system and vehicle locations. Even when Poisson point processes (PPPs) are used to model the vehicle locations on each street, the analysis is barely tractable. That holds for even a simple average-based performance metric—the success probability, which is a special case of the fine-grained metric, the meta distribution (MD) of the signal-to-interference ratio (SIR). To address this issue, we propose the transdimensional approach as an alternative. Here, the union of 1D PPPs on the streets is simplified to the transdimensional PPP (TPPP), a superposition of 1D and 2D PPPs. The TPPP includes the 1D PPPs on the streets passing through the receiving vehicle and models the remaining vehicles as a 2D PPP ignoring their street geometry. Through the SIR MD analysis, we show that the TPPP provides good approximations to the more cumbrous models with streets characterized by Poisson line/stick processes; and we prove that the accuracy of the TPPP further improves under shadowing. Lastly, we use the MD results to control network congestion by adjusting the transmit rate while maintaining a target fraction of reliable links. A key insight is that the success probability is an inadequate measure of congestion as it does not capture the reliabilities of the individual links.

Correspondence identification is an essential problem for collaborative multi-robot perception, with the objective of deciding the correspondence of objects that are observed in the field of view of each robot. In this paper, we introduce a novel maximin hypergraph matching approach that formulates correspondence identification as a hypergraph matching problem. The proposed approach incorporates both spatial relationships and appearance features of objects to improve representation capabilities. It also integrates the maximin theorem to optimize the worst-case scenario in order to address distractions caused by non-covisible objects. In addition, we design an optimization algorithm to address the formulated non-convex non-continuous optimization problem. We evaluate our approach and compare it with seven previous techniques in two application scenarios, including multi-robot coordination on real robots and connected autonomous driving in simulations. Experimental results have validated the effectiveness of our approach in identifying object correspondence from partially overlapped views in collaborative perception, and have shown that the proposed maximin hypergraph matching approach outperforms previous techniques and obtains state-of-the-art performance.

The recent advancements in communication and computational systems have led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally efficient neural networks and high speed wireless vehicular networks have been some of the main contributors to this improvement. However, scalability and reliability issues caused by inherent limitations of sensory and communication systems are still challenging problems. In this paper, we aim to mitigate the effects of these limitations by introducing the concept of feature sharing for cooperative object detection (FSCOD). In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles while maintaining a balance between computation and communication load. This approach is different from current methods of map sharing, or sharing of raw data which are not scalable. The performance of the proposed approach is verified through experiments on Volony dataset. It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches.

ITS-G5 is a communication system for vehicle-to-everything communication for road safety and traffic efficiency applications. On the lower layers, it is based on IEEE 802.11 standard and uses a simple ad hoc mode with a random medium access control scheme. Ad hoc networking is realized by a geographical routing scheme that provides single- and multi-hop communication over ITS-G5 links for periodic and event-driven broadcast messages. Specifically, contention-based forwarding (CBF) allows for efficient and reliable multi-hop packet transport by overhearing and timer-based transmission control. To cope with the channel congestion, decentralized congestion control (DCC) adjusts the message rate and ensures that the network load keeps below a predefined threshold of the bandwidth. To enforce a node's message rate, a “ Gatekeeper” above the MAC and beneath CBF is added. Under high network load, this Gatekeeper introduces an additional queuing delay seen by CBF, which can cause its overhearing function to fail. This contribution studies multi-hop forwarding in the context of DCC with the Gatekeeper and LIMERIC as a rate adaptation algorithm. We propose a congestion-enabled forwarding scheme that, in comparison to existing approaches considering DCC and forwarding separately, restores the efficient operation of CBF, and improves the communication performance in terms of reliability and latency for mixed data traffic composed of single- and multi-hop packets with different priorities. The simulations show the performance improvements in a freeway scenario. An analysis assesses the boundary conditions for CBF and the Gatekeeper. Moreover, the analysis corroborates the simulation.

Localizing occluded object is a long-term challenge in Advanced Driving Assistant System (ADAS) and autonomous driving research. In this paper, we propose a novel graph-matching based approach that leverages the challenge by adopting the deep learning and multiple-view geometry analysis. Specifically, the 3D scene reconstruction is firstly built by associating the comprehensive graph representations of the multiple-view observations, incorporated with the spatial relationship of the co-visible objects so as their discriminant appearance features. Followed by, the localization for occluded object is achieved by inferring from the reconstructed 3D geometry. We conduct experiments to validate the system in connected vehicular platform in the advanced traffic simulation dataset. The experimental results convincingly indicate the effectiveness of the proposed system in real- time object detection, graph generation, matching and location inference for occluded objects.

This paper proposes a stochastic model for the Global Positioning System (GPS) position errors on smartphones in urban canyons. The need to simulate such errors arises in pedestrian to vehicle communications, for example, which enable a new way to protect vulnerable road users. Studying this technology requires accurate modeling of the GPS precision on portable devices, e.g., smartphones since they share pedestrians' location information with vehicles for collision avoidance. The model is derived from and calibrated with pedestrian GPS traces collected from New York City. We show that the model produces GPS error samples with similar spatial and temporal correlation as in the collected field data.

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.

Pedestrians are particularly vulnerable traffic participants with a very high fatality rate. An important component of Advanced Driver Assistant Systems is the accurate localization of pedestrians and other vulnerable traffic participants using various sensors and other technologies. Localization using radio frequency (RF) signals is commonly used, because of the widespread availability of wireless radios, ease of deployment, and the fact that RF-based ranging will work in all weather and light conditions. However, the accuracy of RF-based ranging in vehicular networks is easily affected by high device dynamics and mobility, leading to varying impacts of shadowing and multipath fading. In this paper, we introduce a new ranging algorithm that first uses a novel filtering scheme that reduces the impact of mutipath fading and shadowing. Then, we use an exhaustive search method to minimize an MSE function that represents the difference between the filtered RSSI values and the expected RSSI values (based on the channel model) to estimate the distance between transmitter and receiver. The performance of the proposed ranging algorithm is evaluated using real field measurements in terms of accuracy and convergence time.

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.

Channel congestion is one of the major challenges for IEEE 802.11p-based vehicular ad hoc networks. To tackle the challenge, several algorithms have been proposed and some of them are being considered for standardization. Situations could arise where vehicles with different algorithms operate in the same network. Our previous work has investigated the performance of a mixed-algorithm vehicular network for the CAM-DCC and LIMERIC algorithms and identified that the CAM-DCC vehicles could potentially experience a performance degradation after introducing the LIMERIC vehicles into the network. In this work, we study whether it is possible to eliminate or bound this degradation. We propose a CBP target adjustment mechanism which controls the CBP target of LIMERIC vehicles according to vehicle density and mixing situation of the two algorithms in the network to limit the performance degradation of CAM-DCC vehicles to a desired level. The proposed mechanism is evaluated via both MATLAB and ns-2 simulations and the simulation results indicate that the performance degradation of the CAM-DCC vehicles is controlled as expected with only negligible impact on the performance of LIMERIC vehicles, which still perform similar or better than CAM-DCC vehicles.

Vehicle-to-vehicle safety communications based on the dedicated short-range communication technology have the potential to enable a set of applications that help avoid traffic accidents. The performance of these applications, largely affected by the reliability of communication links, stringently ties back to the MAC and PHY layer design, which has been standardized as IEEE 802.11p. The link reliabilities depend on the signal-to-interference-plus-noise ratio (SINR), which, in turn, depends on the locations and transmit power values of the transmitting nodes. Hence, an accurate network model needs to take into account the network geometry. For such geometric models, however, there is a lack of mathematical understanding of the characteristics and performance of IEEE 802.11p. Important questions such as the scalability performance of IEEE 802.11p have to be answered by simulations, which can be very time consuming and provide limited insights to future protocol design. In this paper, we investigate the performance of IEEE 802.11p by proposing a novel mathematical model based on queuing theory and stochastic geometry. In particular, we extend the Matérn hard-core type-II process with a discrete and nonuniform distribution, which is used to derive the temporal states of backoff counters. By doing so, concurrent transmissions from nodes within the carrier sensing ranges of each other are taken into account, leading to a more accurate approximation to real network dynamics. A comparison with Network Simulator 2 (ns2) simulations shows that our model achieves a good approximation in networks with different densities.

A novel multichannel MAC protocol to solve exposed terminal problem is presented for efficient channel sharing in multihop wireless networks. It employs request-to-send and clear-to-send (RTS/CTS) dialogue on a common channel and flexibly selects conflict-free traffic channel to transmit data packet based on a novel channel selection scheme. The acknowledgment (ACK) packet is transmitted over another common channel, which effectively eliminates the influence of exposed terminal problem. Finally, simulation results show that the proposed protocol outperforms the CAM-MAC protocol on multiple access performance.