MHAV: multitier heterogeneous adaptive vehicular network with LTE and DSRC

Enabling cooperation between vehicles form vehicular networks, which provide safety, traffic efficiency and infotainment. The most vital of these applications require reliability and low latency. Considering these requirements, this paper presents a multitier heterogeneous adaptive vehicular (MHAV) network. Comprising of transport operator or authority owned vehicles in high tier and all the other privately owned vehicles in low tier, integrating cellular network with dedicated short range communications. The proposed framework is implemented and evaluated in Glasgow city center model. Simulation results demonstrate that the proposed architecture outperforms previous multitier architectures in terms of latency while offloading traffic from cellular networks

Symbol-level Precoding in MISO Broadcast Channels for SWIPT Systems

This work investigates a problem for joint transmit beamforming and receive power splitting in multiple-input single-output downlink systems under quality of service and power transfer constraints. Rather than suppressing interference as in conventional schemes, this work takes advantage of constructive interference among users, inherent in the downlink, as a source of both useful information signal energy and electrical wireless energy. Specifically, we propose a new data-aided precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference, based on the constructive regions in the signal constellation. Although the resulting problem is nonconvex, we propose second-order cone programming algorithms with polynomial complexity that provide upper and lower bounds to the optimal solution and establish the asymptotic optimality of these algorithms when the modulation order and signal to interference-plus-noise ratio threshold tend to infinity. Simulation results show significant power savings with the proposed data-aided precoding approach compared to the conventional precoding scheme

Novel Mode Selection Schemes for Buffer-Aided Cooperative NOMA System

This paper investigates a cooperative non-orthogonal multiple access (C-NOMA) system, where the NOMA and buffer-aided cooperative transmission modes between the users are integrated. Two novel mode selection schemes are proposed, which adaptively select the NOMA and cooperative modes according to different buffer states and communication environments. These two proposed schemes are termed single-core state (SCS) and dual-core state (DCS) schemes since they correspond to single and dual-core buffer states. These core states are carefully chosen, which ensure not only a sufficient amount of available transmission modes or links but also a small number of stored packets at each buffer. The closed-form expressions of the outage probabilities and average delays of the proposed schemes are derived and verified by simulation results. Asymptotic performance analysis is also performed, revealing that both proposed schemes achieve the full diversity within the minimum required buffer size of two. Analytical and simulation results show that the proposed SCS and DCS schemes ensure favourable outage performance and the lowest delay, respectively

Fast Meta Learning for Adaptive Beamforming

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance

Embedding Model-Based Fast Meta Learning for Downlink Beamforming Adaptation

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta learning can deal with the task mismatch, it relies on labelled data and incurs high complexity in the pre-training and fine tuning stages. We propose a simple yet effective adaptive framework to solve the mismatch issue, which trains an embedding model as a transferable feature extractor, followed by fitting the support vector regression. Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation. The effectiveness of the proposed method is verified through two well-known applications, i.e., the signal to interference plus noise ratio balancing problem and the sum rate maximization problem. Furthermore, we extend our proposed method to online scenarios in non-stationary environments. Simulation results demonstrate the advantages of the proposed algorithm in terms of both performance and complexity. The proposed framework can also be applied to general radio resource management problems

On Security and reliability using cooperative transmissions in sensor networks

Cooperative transmissions have received recent attention and research papers have demonstrated their benefits for wireless networks. Such benefits include improving the reliability of links through diversity and/or increasing the reach of a link compared to a single transmitter transmitting to a single receiver (single-input single-output or SISO). In one form of cooperative transmissions, multiple nodes can act as virtual antenna elements and provide diversity gain or range improvement using space-time coding. In a multi-hop ad hoc or sensor network, a source node can make use of its neighbors as relays with itself to reach an intermediate node with greater reliability or at a larger distance than otherwise possible. The intermediate node will use its neighbors in a similar manner and this process continues till the destination is reached. Thus, for the same reliability of a link as SISO, the number of hops between a source and destination may be reduced using cooperative transmissions as each hop spans a larger distance. However, the presence of ma-licious or compromised nodes in the network impacts the benefits obtained with cooperative transmissions. Using more relays can increase the reach of a link, but if one or more relays are malicious, the transmission may fail. However, the relationships between the number of relays, the number of hops, and success probabilities are not trivial to determine. In this paper, we analyze this problem to understand the conditions under which cooperative transmissions fare better or worse than SISO transmissions. We take into consideration additional parameters such as the path-loss exponent and provide a framework that allows us to evaluate the conditions when cooperative transmissions are better than SISO transmissions. This analysis provides insights that can be employed before resorting to simulations or experimentation. © Springer Science+Business Media, LLC 2012

HopScotch - a low-power renewable energy base station network for rural broadband access

The provision of adequate broadband access to communities in sparsely populated rural areas has in the past been severely restricted. In this paper, we present a wireless broadband access test bed running in the Scottish Highlands and Islands which is based on a relay network of low-power base stations. Base stations are powered by a combination of renewable sources creating a low cost and scalable solution suitable for community ownership. The use of the 5~GHz bands allows the network to offer large data rates and the testing of ultra high frequency ``white space'' bands allow expansive coverage whilst reducing the number of base stations or required transmission power. We argue that the reliance on renewable power and the intelligent use of frequency bands makes this approach an economic green radio technology which can address the problem of rural broadband access