Coordinated signal processing can obtain a huge transmission gain for Fog Radio Access Networks (F-RANs). However, integrating into large scale, it will lead to high computation complexity in channel estimation and spectral efficiency loss in transmission performance. Thus, a joint cluster formation and channel estimation scheme is proposed in this paper. Considering research remote radio heads (RRHs) centred serving scheme, a coalition game is formulated in order to maximize the spectral efficiency of cooperative RRHs under the conditions of balancing the data rate and the cost of channel estimation. As the cost influences to the necessary consumption of training length and estimation error. Particularly, an iterative semi-blind channel estimation and symbol detection approach is designed by expectation maximization algorithm, where the channel estimation process is initialized by subspace method with lower pilot length. Finally, the simulation results show that a stable cluster formation is established by our proposed coalition game method and it outperforms compared with full coordinated schemes.

As a promising paradigm of the fifth generation networks, fog radio access network (F-RAN) has attracted lots of attention nowadays. To fully utilize the promising gain of F-RANs, the acquisition of accurate channel state information is significant. However, conventional channel estimation approaches are not suitable in F-RANs due to the large training and feedback overhead. In this paper, we consider the channel estimation in F-RANs with fog access point (F-AP) equipped with massive antennas. Thanks to the computing ability of F-AP and the sparsity of channel matrices in angular domain, Gated Recurrent Unit (GRU), a data-driven based channel estimation is proposed at F-AP to reduce the training and feedback overhead. The GRU-based method can capture the hidden sparsity structure automatically through the network training. Moreover, to further improve the channel estimation, a bidirectional GRU based method is proposed, whose target channel structure is decided by previous and subsequent structures. We compare the performance of our proposed channel estimation with traditional methods (Orthogonal Matching Pursuit (OMP) and Simultaneous OMP (SOMP)). Simulation results show that the proposed approaches have better performance compared with the traditional OMP and SOMP methods.

Through integrating advanced communication and data processing technologies into smart vehicles and roadside infrastructures, the Intelligent Transportation System (ITS) has evolved as a promising paradigm for improving safety, efficiency of the transportation system. However, the strict delay requirement of the safety-related applications is still a great challenge for the ITS, especially in dense traffic environment. In this paper, we introduce the metric called Perception-Reaction Time (PRT), which reflects the time consumption of safety-related applications and is closely related to road efficiency and security. With the integration of the incorporating information-centric networking technology and the fog virtualization approach, we propose a novel fog resource scheduling mechanism to minimize the PRT. Furthermore, we adopt a deep reinforcement learning approach to design an on-line optimal resource allocation scheme. Numerical results demonstrate that our proposed schemes is able to reduce about 70% of the RPT compared with the traditional approach.

In this paper, we investigate vehicular fog computing system and develop an effective parallel offloading scheme. The service time, that addresses task offloading delay, task decomposition and handover cost, is adopted as the metric of offloading performance. We propose an available resource-aware based parallel offloading scheme, which decides target fog nodes by RSU for computation offloading jointly considering effect of vehicles mobility and time-varying computation capability. Based on Hidden Markov model and Markov chain theories, proposed scheme effectively handles the imperfect system state information for fog nodes selection by jointly achieving mobility awareness and computation perception. Simulation results are presented to corroborate the theoretical analysis and validate the effectiveness of the proposed algorithm.

The protocol is the foundation of IoT technology, which plays an important role in the IoT device interworking and interoperability. The 5G wireless communication system provides large-scale NB-IoT terminals access with F-RAN, and the vertical industry applications need support for the device management. As one of the most influential protocol in the IoT application, the oneM2M protocol not only has a complete architecture, but also has an interface with other IoT protocols. Therefore, to bridge the gap between the operator and industrial enterprises, the main contributions of this paper are as follows: Firstly, a general multi-protocol conversion method is proposed based on oneM2M platform where the protocol classification is used in different scenarios. Secondly, the F-RAN architecture of oneM2M platform is designed and implemented with NB-IoT device access. Thirdly, a multiplexing scheme to process the device information is proposed for interworking proxy entity (IPE), which improves the conversion efficiency for different protocols. Finally, the feasibility and efficiency of the scheme are verified.

Smart communities are an emerging communication means in which humans and smart devices will interact with each other and deliver ubiquitous services by exploiting social intelligence. Distributed antenna system (DAS), one of the key technologies to realize smart decisions in smart communities, can settle network smart coverage problem and improve system energy/spectrum efficiency significantly. Considering that energy consumption is an important element for community communications, in this paper, we survey the existing green DAS research for smart communities. In particular, our investigation covers antenna distribution, system capacity, spectrum efficiency, energy efficiency, and green access issues. Moreover, we analyze the existing application opportunities and challenges. This survey contributes to better understanding of the challenges and approaches for green DAS in existing smart community networks and further shed novel light on some future research directions.

Controller placement problem (CPP) is a critical issue in software defined wireless networks (SDWN). Due to the limited power of wireless devices, CPP is facing the challenge of energy efficiency in SDWN. Nevertheless, the related research on CPP in SDWN hasn’t modeled the energy consumption of controllers so far. To prolong the lifetime of SDWN and improve the practicability of research, we rebuilt a CPP model considering the minimal transmitted power of controllers. An adaptive controller placement algorithm (ACPA) is proposed with the following two stages. First, data field method is adopted to determine sub-networks for different network topologies. Second, for each sub-network we adopt an exhaustive method to find the optimal location which meets the minimal average transmitted power to place controller. Compared with the other algorithms, the effectiveness and efficiency of the proposed scheme are validated through simulation.

Rate Compatible Modulation (RCM) is an efficient technique for high spectral efficiency seamless transmission over highly dynamic wireless channels. However, its high decoding complexity at the receiver prevents it from being applied in scenarios where computation resources are limited. To alleviate this problem, an RCM with variable weight sets (RCM-VWS) was presented in the literature to significantly reduce its complexity by employing weight sets of different complexities for channels at different signal-to-noise-ratio (SNR). However, RCM-VWS has to introduce an undesired feedback channel for the transmission of SNR information that is estimated at the receiver and transmitted back to the transmitter for weight set selection. To achieve a low computational complexity while avoiding feedback transmission at the same time, a novel RCM scheme with hybrid weight set (RCM-HWS) is introduced in this paper. A low complexity of decoding is allowed by gradually reducing the complexity of weight sets based on the number symbols already sent. It also avoids the feedback of SNRs since we can deduce SNRs from the number of the symbols transmitted and use a hybrid weight set we have designed. The theoretical analysis and simulation results show that the proposed scheme has the advantages of low demodulation complexity and not requiring feedback channel while maintaining the same transmission throughput as that of the conventional RCM. Therefore, the proposed scheme has a wider range of applications, especially in the case that feedback channel is not available.

The computation resources at a single node in Edge Computing (EC) are commonly limited, which cannot execute large scale computation tasks. To face the challenge, an Offloading scheme leveraging on NEighboring node Resources (ONER) for EC over Fiber-Wireless (FiWi) access networks is proposed in this paper. In the ONER scheme, the FiWi network connects edge computing nodes with fiber and converges wireless and fiber connections seamlessly, so that it can support the offloading transmission with low delay and wide bandwidth. Based on the ONER scheme supported by FiWi networks, computation tasks can be offloaded to edge computing nodes in a wider range of area without increasing wireless hops (e.g., just one wireless hop), which achieves low delay. Additionally, an efficient Computation Resource Scheduling (CRS) algorithm based on the ONER scheme is also proposed to make offloading decision. The results show that more offloading requests can be satisfied and the average completion time of computation tasks decreases significantly with the ONER scheme and the CRS algorithm. Therefore, the ONER scheme and the CRS algorithm can schedule computation resources at neighboring edge computing nodes for offloading to meet the challenge of large scale computation tasks.

In this paper, an energy harvesting enabled cooperative non-orthogonal multiple access (NOMA) system for a multi-cell network is investigated. Particularly, during the direct transmission phase, base stations send their superposed messages to the near users and far users simultaneously according to a NOMA principle, while the near users act as energy harvesting enabled relays employing a power splitting protocol. During the cooperative phase, the near users transmit their decoded messages to the corresponding far users using harvested energy. Using tools from stochastic geometry, we firstly calculate the signal to interference ratios of the users in each NOMA group including one near user and one far user. Then, the closed-form expressions of the coverage probability, ergodic rate, and energy efficiency are derived respectively. Numerical results validate the derived expressions and show that the energy harvesting enabled cooperative NOMA system in a multi-cell network can improve the coverage probability, ergodic rate, and energy efficiency compared to its counterpart OMA system.

In this paper, the transmission performances are studied in cognitive radio networks with primary user emulator and relay existence. In the proposed network, the users include primary users, secondary users and primary user emulators. The decreasing access priority of the users are primary users, primary user emulators and secondary users. Different user access to the network results in different transmission effects. We impose interference power constraints on the secondary users to protect the primary users from being interfered. We also adopt the transmission mechanism that transits among more than one secondary transmitters, secondary receivers and relays. The transition models of the transmission states are proposed to describe the transmission mechanism. To investigate the transmission performances, the theory of effective capacity is adopted. The transmission performances in terms of effective capacity are expressed and demonstrated under different transmission policies. The overall effective capacity, as the overall data traffic in the cognitive radio network, is calculated. Besides, the overall effective capacity is demonstrated under different transmission strategies. The results show the greedy transmission strategy outperforms the rest of the transmission policies in the overall effective capacity. For a larger number of the users, the effective capacity converges to a certain value.

Fog radio access network (F-RAN) is one of the key technology that brings cloud computing benefit to the future of wireless communications for handling massive access and high volume of data traffic. The high fronthaul burden of a typical cellular system can be partially diminished by utilizing the storage and signal processing capabilities of the F-RANs, which is still not desirable as user throughput requirement is in the increasing trend with the increment of the internet of things (IoT) devices. This paper proposes an efficient scheduling scheme that minimizes the fronthaul load of F-RAN system optimally to improve user experience, and minimize latency. The scheduling scheme is modeled in a way that the scheduler which provides the lower fronthaul load while fulfilling the minimum user throughput requirement is selected for the data transmission process. Simulation results in terms of user selection fairness, outage probability, and fronthaul load for a different portion of user equipments (UEs) contents in fog access point (F-AP) are shown and compared with the most common scheduling scheme such as round robin (RR) scheme to validate the proposed method.

This paper proposes a multi-scale self-recovery (MSSR) approach to protect images against content forgery. The main idea is to provide more resistance against image tampering while enabling the recovery process in a multi-scale quality manner. In the proposed approach, the reference data composed of several parts and each part is protected by a channel coding rate according to its importance. The first part, which is used to reconstruct a rough approximation of the original image, is highly protected in order to resist against higher tampering rates. Other parts are protected with lower rates according to their importance leading to lower tolerable tampering rate (TTR), but the higher quality of the recovered images. The proposed MSSR approach is an efficient solution for the main disadvantage of the current methods, which either recover a tampered image in low tampering rates or fails when tampering rate is above the TTR value. The simulation results on 10000 test images represent the efficiency of the multi-scale self-recovery feature of the proposed approach in comparison with the existing methods.

The ever increasing demand of ad-hoc networks for adaptive topology and mobility aware communication led to new paradigm of networking among Unmanned Aerial Vehicles (UAVs) known as Flying ad-hoc Networks (FANETs). Due to their dynamic topology, FANETs can be deployed for disaster monitoring and surveillance applications. During these operations, UAVs need to transmit different disaster data, which consists of different types of data packets. Among them there are packets which need to be transmitted urgently because of the emergency situation in disaster management. To handle this situation, we propose a methodology of disaster data classification using urgency level and based on these urgency levels, priority index is assigned to data packets. An approach of Urgency Aware Scheduling (UAS) is proposed to efficiently transmit high and low priority packets with minimum delays in transmission queue. We take into account different scenarios of UAVs for disaster management and for N number of UAVs, we propose bio-inspired mechanism using behavioral study of bird flocking for cluster formation and maintenance. Furthermore, we propose a priority based route selection methodology for data communication in FANET cluster. Simulation results show that our proposed mechanism shows better performance in the presence of evaluation benchmarks like average delay, queuing time, forward percentage and fairness.

The limited labeled sample data in the field of advanced security threats detection seriously restricts the effective development of research work. Learning the sample labels from the labeled and unlabeled data has received a lot of research attention and various universal labeling methods have been proposed. However, the labeling task of malicious communication samples targeted at advanced threats has to face the two practical challenges: the difficulty of extracting effective features in advance and the complexity of the actual sample types. To address these problems, we proposed a sample labeling method for malicious communication based on semi-supervised deep neural network. This method supports continuous learning and optimization feature representation while labeling sample, and can handle uncertain samples that are outside the concerned sample types. According to the experimental results, our proposed deep neural network can automatically learn effective feature representation, and the validity of features is close to or even higher than that of features which extracted based on expert knowledge. Furthermore, our proposed method can achieve the labeling accuracy of 97.64%~98.50%, which is more accurate than the train-then-detect, kNN and LPA methods in any labeled-sample proportion condition. The problem of insufficient labeled samples in many network attack detecting scenarios, and our proposed work can function as a reference for the sample labeling tasks in the similar real-world scenarios.

In order to improve the sensitivity of the Compass B1C signal acquisition for the receiver, the principle of constant false alarm rate (CFAR) is applied for the B1C pilot channel acquisition to realize the dynamic adjustment of the threshold of acquisition against the carrier to noise ratio. The non-coherent data/pilot combined acquisition algorithm for B1C signal is analyzed to make full use of the power of the B1C signal under the condition of low carrier to noise ratio. On this basis, to improve the acquisition sensitivity of the receiver, the principle of constant false alarm probability is applied for the non-coherent data/pilot combined acquisition algorithm. Theoretical analysis and simulations show that the non-coherent data/pilot combined acquisition algorithm with CFAR improves the B1C signal acquisition sensitivity of the receiver significantly, and achieves a better Receiver Operating Characteristic compared with the traditional acquisition algorithms.

Semantic segmentation of urban scenes is an enabling factor for a wide range of applications. With the development of deep learning in recent years, semantic segmentation tasks using high-capacity models have achieved considerable successes on large datasets. However, the pixel-level annotation process, especially for urban scene images with various objects, is tedious and labor intensive. Meanwhile, the scale of the unlabeled data, which is currently easy to collect, is often much larger than labeled data. Thus, using the abundant unlabeled data to make up the loss of the segmentation model from insufficient labeled data is of great interest. In this paper, we propose a semi-supervised method based on reinforcement learning to capture the contextual information from the unlabeled data to improve the model trained on the small scale labeled data. Both quantitative and qualitative experiments have shown the effectiveness of the proposed method.