To improve spectrum efficiency without interfering with licensed users, reliable prediction of spectrum occupancy plays a pivotal role in a dynamic spectrum allocation (DSA) system. A reliable machine learning method capable of exploring the long-term correlation in the data is using a neural network with long short-term memory (LSTM). However, in the situation that there are multiple sensors in the network, how to effectively exploit the spatial correlation among these sensors' data for accurate spectrum prediction remains an open issue. Directly applying LSTM to multiple series may even reduce the prediction accuracy if some series are uncorrelated. In this article, we propose a method of clustering to aid in predicting multi-dimensional received power based on the hierarchical agglomerative clustering (HAC) model, which clusters the correlated series with high spearman's rank correlation coefficient (SRCC). Similar series are grouped into clusters and trained to predict independently using HAC. By ensuring uncorrelated series do not influence each other, the LSTM prediction accuracy is improved. A low-pass filter is used to remove high-frequency noise components and further reduce the prediction error. Experimental results show that our method significantly increases the prediction accuracy in all cases.

In vehicular ad hoc networks (VANETs), beacon broadcasting plays a critical role in improving road safety and avoiding hazardous situations. How to ensure reliability and scalability of beacon broadcasting is a difficult and open problem, due to high mobility, dynamic network topology, hidden terminal, and varying density in both the time and location domains. In this paper, wireless resources are divided into basic resource units in the time and frequency domains, and a distributed and adaptive reservation based MAC protocol (DARP) is proposed to solve the above problem. For decentralized control in VANETs, each vehicle's channel access is coordinated with its neighbors to solve the hidden terminal problem. To ensure the reliability of beacon broadcasting, different kinds of preambles are applied in DARP to support distributed reservation, detect beacon collisions, and resolve collisions. Once a vehicle reserves a resource unit successfully, it will not release it until collision occurs due to topology change. The protocol performance in terms of access collision probability and access delay are analyzed. Based on the analysis, protocol parameters, including transmission power and time slots duration, can be adjusted to reduce collision probability and enhance reliability and scalability. Using NS-3 with vehicle traces generated by simulation of urban mobility (SUMO), simulation results show that the proposed DARP protocol can achieve the design goals of reliability and scalability, and it substantially outperforms the existing standard solutions.

To support emerging vehicular applications such as autonomous driving, the reliability of beacon broadcasting becomes an indispensable issue which is also difficult given the highly dynamic topology and vehicle density. For the territories which are not covered with communication infrastructure, controlling and scheduling in a centralized way may not be applicable. Therefore, vehicle-to-vehicle (V2V) ad hoc communication is promising to address the ubiquitousness coverage concern. In this paper, a distributed network coding-based medium access control protocol (NC-MAC) is proposed to support V2V beacon broadcasting. We combine the preamble-based feedback mechanism, retransmissions, and network coding together to enhance broadcasting reliability. Extensive simulations are given to show the performance gain of the NC-MAC protocol compared to the existing 5G cellular vehicle-to-everything (C-V2X) MAC protocol in a wide range of scenarios.

Radio spectrum is a limited and increasingly scarce resource, which motivates alternative usage methods such as dynamic spectrum allocation (DSA). DSA of a frequency band requires an accurate prediction of spectrum usage in both the time and spatial domains with minimal sensing cost. In this paper, we address challenge in two steps. First, in order to make the best use of the limited sensors in the region, we deploy a deep learning prediction model based on convolutional neural networks (CNNs) and residual networks (ResNets), to predict spatio-temporal spectrum usage at the sensors' locations. Second, given an area enclosed by a few sensors, a Bayesian estimation model is proposed to first derive the location distribution of a transmitter, and then obtain the interference power distribution within the area. Simulation results show the efficacy and efficiency of the proposed prediction models.

Vehicle-to-everything (V2X) technology has been proposed as a leading step toward road safety improvement and intelligent transportation systems, and these vehicular-related applications rely on reliable ways of communication. For the regions which are not covered by communication infrastructure or deployment of centralized controllers are not straightforward, vehicle-to-vehicle (V2V) communication is a potential solution. Reliability is crucial in vehicular ad hoc networks (VANETs) from beacon broadcasting and security aspects. Group-casting and applying multi-hop communication can ensure reliability in V2X systems. In this paper, a distributed grouping and network coding-assisted medium access control protocol (GNC- MAC) is proposed to support reliable group-casting. We propose a new grouping protocol by combining preamble-based feedback mechanism, multi-hop communication, and network coding to improve group-casting reliability. Extensive simulations have been conducted to show the performance gain of the GNC- MAC protocol compared to the existing 5G cellular vehicle-to- everything (C-V2X) MAC protocol in a wide range of scenarios.

The growth of Internet of Things (IoT) is anticipated to accelerate in the coming years. However, the wireless spectrum is insufficient to support the ever-growing IoT applications. A promising solution is to allow concurrent wireless transmissions and decode the superimposed signal. To make this solution practical for IoT systems, dynamic channel conditions and hardware imperfections are the key practical challenges, but not yet be addressed in the past work, leading to a low decoding performance. In this article, we introduce SigMix, aiming to deal with the practical challenges by proposing a solution to decode the superimposed signal, and eventually boost the spectrum efficiency. To this end, we first derive a theoretical expression that reveals the close relationship between phase shifts among concurrently transmitted signals and the error probability in decoding the superimposed signal. Then, based on the theoretical expression, we propose a rotation code and an adaptive decoding scheme to largely reduce the decoding error probability. Extensive experiments have shown that the median bit-error-rate of our scheme is one-order lower than the state-of-the-art.

Reliable and scalable wireless transmissions for Internet of Vehicles (IoV) are technically challenging. Each vehicle, from driver-assisted to automated one, will generate a flood of information, up to thousands of times of that by a person. Vehicle density may change drastically over time and location. Emergency messages and real-time cooperative control messages have stringent delay constraints while infotainment applications may tolerate a certain degree of latency. On a congested road, thousands of vehicles need to exchange information badly, only to find that service is limited due to the scarcity of wireless spectrum. Considering the service requirements of heterogeneous IoV applications, service guarantee relies on an in-depth understanding of network performance and innovations in wireless resource management leveraging the mobility of vehicles, which are addressed in this article. For single-hop transmissions, we study and compare the performance of vehicle-to-vehicle (V2V) beacon broadcasting using random access-based (IEEE 802.11p) and resource allocation-based (cellular vehicle-to-everything) protocols, and the enhancement strategies using distributed congestion control. For messages propagated in IoV using multihop V2V relay transmissions, the fundamental network connectivity property of 1-D and 2-D roads is given. To have a message delivered farther away in a sparse, disconnected V2V network, vehicles can carry and forward the message, with the help of infrastructure if possible. The optimal locations to deploy different types of roadside infrastructures, including storage-only devices and roadside units with Internet connections, are analyzed.

The wide range of Internet-of-Things applications has increased the number of connected devices massively. This growth may cause more contention in accessing the channel, challenging the legacy IEEE 802.11. In the IEEE 802.11ah standard, the grouping technique is exploited to make the stations (STAs) compete in a group to mitigate the contention. However, how to group the STAs in the network is still an open issue. In this paper, we propose a new strategy to group STAs in a dense network to address the above issues. We apply the MaxMin fairness criterion to the STAs' throughput to increase the overall network's performance with better fairness. Formulation of the problem results in a non-convex integer programming optimization problem which avoids hidden terminals opportunistically. As solving the optimization problem is difficult and time consuming, we apply the Ant Colony Optimization method to the problem to find the solution. Extensive simulations have been conducted to validate the solution. The proposed approach can achieve approximately up to 40% gain in the total throughput, 37% gain in the minimum per-STA throughput in the network, and 11% reduction in the number of hidden terminals compared to the existing strategies such as K-means.

In this paper, we investigate the spectral efficiency of a communication link in a wireless body area network (WBAN) capable of harvesting energy from the environment. We consider two scenarios for the transmission which are single- and dual-hop and achieve the power management policy for each scenario. In the first scenario, the aim is to maximize the link's spectral efficiency over N time slots subject to the battery capacity, energy harvesting constraint, and WBAN limitations including power and outage probability. In the second scenario, a decode-and-forward relay node is considered, and a spectral efficiency optimization problem with constraints similar to the first scenario is evaluated. In addition, since the channel distribution information is available at the transmitters, the lower and upper bounds of the average spectral efficiency are also derived in both scenarios. Finally, numerical results corroborate the analytical results.