A city-wide experimental testbed for the next generation wireless networks

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

This paper studies the downlink of a cloud radio access network (C-RAN) that incorporates a baseband central processor (CP), multiple remote radio units (RUs), and a network of wireless fronthaul links that connect the RUs to the CP. The fronthaul network utilizes dedicated point-to-point free-space optical (FSO) links along with a broadcast radio frequency (RF) channel. The spectrum of the RF channel is also utilized for downlink transmission from the RUs to the mobile users. That is, the available RF spectrum is time-shared (in a half-duplex manner) among the fronthaul and downlink. The data symbols intended for different users are linearly precoded at the CP in the form of quantized in-phase and quadrature (IQ) samples. These samples are compressed then delivered via the fronthaul network to the corresponding RUs. The RUs, in turn, perform decompression and IQ modulation, before broadcasting their RF signals to the users. We focus on the joint design of the linear precoders, quantizers, and capacity of the RF fronthaul links, along with the time allocation of the RF spectrum, in order to maximize the weighted sum-rate of the users, subject to power constraints and capacity limitations of the hybrid fronthaul network. The resulting problem is nonconvex and difficult to handle. Therefore, we propose a computationally-tractable algorithm that utilizes line search and alternating convex optimization in order to obtain a high-quality suboptimal solution. We provide numerical examples to demonstrate the performance of the proposed algorithm under different weather conditions. We also show the performance gain of hybrid RF/FSO fronthaul, as compared to FSO-only fronthaul, during unfavorable weather conditions.

Unleashing the potential of sixth generation (6G) wireless networks in smart energy grid management: A comprehensive review

The incipient vehicular applications and the explosive growth of data generally led to the increase in demand for communication, computation, and storage resources, as well as the stringent performance requirement on latency and wireless network capacity. To address these challenges, Vehicular Edge Computing is an envisioned promising solution that extends the computation capability to the edge of the vehicular network and computation offloading service is provided in the proximity of the vehicles. Vehicular edge computing (VEC) and D2D communication have been designed to benefit from proximity gain to optimize computation offloading. Software-defined network (SDN) inside the vehicular network (SDVN) is developed in this article to rectify most problems in the vehicular network. Besides, the SDVN controller handles D2D pairing of vehicles and co-ordinates with the edge cloud for flexible allocation of computing resources among vehicles and mobile users. In this article, we combine D2D communication and VEC to further enhance the computation capacity of the vehicular network. Our goal is to optimize the number of devices the vehicular network can support under communication and computational limitation. We formulate the task offloading and associated resource allocation problem as a game with mixed strategies. The players involved in the game are the SDN controller handling the computation resource allocation at the network edge and a vehicle deciding on task offloading. The existence and uniqueness of Nash equilibrium are theoretically proved.

Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networksprovides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also:Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and opportunistic networksDemonstrates the practices for designing effective protocol/applications for next generation wireless networksIncludes case studies showcasing the importance of properly understanding fundamental mobility model properties in wireless network performance evaluation

The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users. In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication. IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics. Implementing the above learning methods on the embedded chip leads to high latency, high power consumption and more chip area utilisation. To overcome the problems mentioned above, we present DEEP HOLE Radio systems, the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency (RF) environment. DEEP HOLE Radio provides (i) an optimized deep learning framework with a good trade-off between latency, power and utilization. (ii) Complete Hardware-Software architecture where the SoC’s coupled with radio transceivers for maximum performance. The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni directional antennas. The whole spectrum of knowledge has been extracted using GNU radio. These extracted features are used to train the proposed optimized deep learning models, which run parallel on Zynq-SoC 7000, consuming less area, power, latency and less utilization area. The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn, Long Term Short Memory (LSTM), Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN). The outcome shows that the proposed framework has outperformed the existing framework regarding the area, power and time. Moreover, the experimental results show that the proposed framework decreases the delay, power and area by 15%, 20% 25% concerning the existing RFlearn and other hardware constraint frameworks.

Vehicular networks provide Internet services for car drivers. Multiple radio access technologies are usually used in existent mobile devices and are expected in the next generation wireless networks. A vehicle equips with one cognitive radio interface or WiFi interface can communicate with neighbor vehicles or base stations. It is critical for electrical vehicles to diminish energy consumption cost, besides, reducing energy consumption also reduces green gas emission. In this paper, we propose an energy-efficient transmission scheme in the vehicular networks with cognitive radio and WiFi. In the proposed scheme, we appropriately distribute traffic load to cognitive radio and WiFi interfaces such that traffic load are transmitted in an energy-efficient manner. Numerical results show that the proposed transmission scheme appropriately distributes data into the two radio interfaces such that the power consumption is significantly reduced.

For the past 10 years the Advanced Industrial Materials (AIM) has supported development of new and improved materials to enable U.S. industry to improve energy efficiency, increase productivity, and reduce waste. It has been a National Laboratory based program, with work currently under way at Oak Ridge National Laboratory, Los Alamos National Laboratory, and Sandia National Laboratories, in collaboration with industrial and university partners. With the advent of the Industries of the Future (IOF) strategy within the Office of Industrial Technologies (OIT) and the scheduled completion of the Continuous Fiber Ceramic Composites (CFCC) Program in FY 2002, an integrated materials program is being developed in OIT. So this represents the last summary of AIM research and development. The new program, Industrial Materials for the Future (IMF), will be competitive in operation, with solicitations for proposals for development of materials in accordance with the IOF Technology Roadmaps, followed by merit review and funding of the best proposals. Industry will take the lead in ''industry-specific'' research and development, in cooperation with National Laboratories, as needed. National Laboratories and universities will take the lead in maintaining a base technology program, for the purpose of maintaining a continuing flow of new materials technologies. The AIM and CFCC Programs will be replaced by the IMF program over a three year period, so that in FY 2004, all research and development will be in response to industry solicitations and Laboratory/university calls. The Program Manager believes that AIM has been an extremely successful program, thanks to the Laboratory investigators and their partners. For 10 years, the program has increased industrial participation from very little to nearly 100 percent. The CFCC Program, similarly, has been successful in advancing the knowledge of processing and property development in these materials, though much still can be done in advancing their uses in industry. It is hoped that the Industrial Materials for the Future Program will be equally successful, not only in solving industry's short-term, immediate needs, but also in maintaining a materials technology base that will lead to longer-range materials and processing developments. The projects summarized here will be carried to successful conclusions over the next 3 years and the current Laboratories in AIM and CFCC will be joined by other Laboratories, universities, and new industrial partners. The Program Manager expresses his profound appreciation for the very fine work done for OIT during the last 10 years.

Vehicular Ad Hoc network-VANET plays a crucial role in daily vehicular users and ensures safety for drivers and passengers. But there are some issues before VANET can be utilized. One of those issues is spectrum scarcity i.e., some vehicular users have insufficient spectrum which is used for their communication. In the United States of America, the Federal Communications Commission (FCC) allocated the 75 MHz in the 5.850–5.925 GHz band covering 7 channels with 10 MHz each and 5 MHz reserved as a guard band. So, to overcome the above issue we have Dynamic spectrum access (DSA) via Cognitive radio technology. CR main function is to find the spectrum holes in licensed frequency channels so that unlicensed spectrum users can utilize the unused spectrum. But due to their high speed and topology there are some challenges for spectrum sensing which are as follows. At first to develop effective spectrum sensing solution for vehicular networks we present Existing data for complete classification of CVN spectrum sensing techniques and next we talk about Results of the automotive environment like speed, traffic density fading on spectrum sensing, at last we proceed to a set of prerequisite for CVN spectrum techniques that gives specific cognitive vehicular networks environment characteristics.

The number of base stations (BSs) for the fifth generation (5G) wireless network is substantially increased, as each coverage is greatly reduced. Therefore, both the miniaturization and the densification of BSs suffer from the challenges of electrical power supply and deployment cost. Here, we present an optically powered 5G fronthaul network, in support of the co-propagation of spatial-division-multiplexing (SDM) energy light and wavelength-division-multiplexing (WDM) 5G new radio (NR) signals over the weakly-coupled multicore fiber (WC-MCF). When the 60-W energy light at 1064.8-nm is equally distributed among the outer six cores, and the 9-Gbit/s 5G NR WDM signals are transmitted over the central core of 1-km WC seven-core fiber (WC-7CF), we can collect total 11.9-W electrical power at the remote node, for the purpose of optically powered small cells. Meanwhile, the error-vector magnitude (EVM) values of 1.5-Gbit/s 5G NR 64-level quadrature amplitude modulation orthogonal frequency division multiplexing (64QAM-OFDM) signals at the central frequency of 3.5 GHz fluctuate within a range of 0.3%∼0.39%, under a received electrical power of -25 dBm, for all six-wavelength channels. Six optically powered small cells are equipped with the characteristics of centralized management and flexible access-rate.

5G is coming with a promise to provide ubiquitous coverage with high data rate availability. To do so, densification of access points to enhance the system capacity is anticipated. For managing such densely populated network, 5G will be employing Centralized Radio Access Network (CRAN), where most of the Radio Access Network (RAN) functionalities are centralized in a central processing unit. This centralization reduces operational costs and eases implementation of advanced technologies, such as, Cooperative multipoint (CoMP) and enhanced inter-cell interference coordination (eICIC), in a cost efficient way. However, CRAN imposes stringent requirements on the fronthaul, i.e. the link connecting access points to the central unit, in terms of capacity and latency. Furthermore, future fronthaul networks are expected to rely on wireless technologies, since wired options are costly, not scalable and not always suitable for all scenarios. Therefore, meeting the expected requirements of fronthaul network utilizing capacity-limited wireless technologies may become an inescapable bottleneck. In this paper, we study different functional splits at the PHY layer in terms of data rate requirements and operational cost, and discuss the combination of different splits aimed at minimizing the overall cost and maximizing the centralization gains, while keeping the capacity requirements below the limit of the fronthaul.

In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks.

Facing the explosion of mobile data traffic, cloud radio access network (C-RAN) is proposed recently to overcome the efficiency and flexibility problems with the traditional RAN architecture by centralizing baseband processing. However, there lacks a mathematical model to analyze the statistical multiplexing gain from the pooling of virtual base stations (VBSs) so that the expenditure on fronthaul networks can be justified. In this paper, we address this problem by capturing the session-level dynamics of VBS pools with a multi-dimensional Markov model. This model reflects the constraints imposed by both radio resources and computational resources. To evaluate the pooling gain, we derive a product-form solution for the stationary distribution and give a recursive method to calculate the blocking probabilities. For comparison, we also derive the limit of resource utilization ratio as the pool size approaches infinity. Numerical results show that VBS pools can obtain considerable pooling gain readily at medium size, but the convergence to large pool limit is slow because of the quickly diminishing marginal pooling gain. We also find that parameters such as traffic load and desired Quality of Service (QoS) have significant influence on the performance of VBS pools.

Vehicular Ad hoc Network (VANET) has become a very interesting technology for future generation vehicles. This is mainly due to its embedded and promising advantages which primarily include improvement of safety conditions on public road through exchange of critical safety messages amongst involved nodes. The success of VANET heavily relies on successful messages dissemination amongst involved vehicles. However, a successful message exchange can only be possible if the transmission medium is collision free. Under the Intelligent Transportation System (ITS), the cooperative Awareness messages (CAM) have to be transmitted at the rate of 10 Hz as per standard. To account for congestion management, the Distributed Congestion Control (DCC) mechanism was proposed. However, under higher node density, the DDC becomes inefficient and dramatically contribute to the deterioration of the VANET environment. The present work proposes a Dynamic Broadcast Storm Mitigation Algorithm (DBSMA) which can be used to combat the broadcast storm problem in a Vehicular Network (VN). Results from several simulations confirmed that the DBSMA has a potentiality to conquer the effect of broadcast storm by offering more than 150% improved efficiency against the DCC approach. Another advantage of the DBSMA is that it is simple to compute and easy to implement.

In view to reduce accident risks and improve safety on public roads, the vehicular Ad hoc network (VANET) standard was developed to enable inter-vehicular communication and facilitate safety message exchange among the involved nodes. Moreover, a successful message exchange can only be possible if the transmission medium is collision free. Under the Intelligent Transportation System (ITS), the cooperative Awareness messages (CAM) have to be transmitted at the rate of 10 Hz as per standard. To account for congestion management, the Distributed Congestion Control (DCC) mechanism was proposed. However, under higher node density, the DDC becomes inefficient and dramatically contribute to the deterioration of the VANET environment. The present work proposes a Dynamic Broadcast Storm Mitigation Algorithm (DBSMA) which can be used to combat the broadcast storm problem in a Vehicular Network (VN). Results from several simulations confirmed that the DBSMA has a potentiality to conquer the effect of broadcast storm by offering more than 130% improved efficiency against the DCC approach. Other advantage of the DBSMA is that it is very simple to compute and easy to implement.