Wireless Interference Classification with Low Complexity Multi-Branch Networks

In non-cooperative communication systems,wireless interference classification(WIC)is one of the most essential technologies.Recently,deep learning(DL)based WIC methods have been proposed.However,conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy,especially when the interference-tonoise ratio(INR)is low.To this end,we propose three effective approaches.Firstly,we introduce multibranch convolutional neural networks(CNNs)for interference recognition.The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology,and it notably improves the recognition ability for WIC.Our design avoids the carefully crafted selection of each transformation.Unfortunately,multi-branch CNNs are computationally expensive and memory-inefficient.To this end,we further propose Low complexity multibranch networks(LCMN),which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference.Thirdly,we present novel loss function,which encourages networks to have consistent prediction probabilities for samples with high visual similarities,resulting in increasing recognition accuracy of LCMN.Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.

Energy-efficient multipath routing in wireless sensor network considering wireless interference

Due to the energy and resource constraints of a wireless sensor node in a wireless sensor network (WSN), design of energy-efficient multipath routing protocols is a crucial concern for WSN applications. To provide high-quality monitoring information, many WSN applications require high-rate data transmission. Multipath routing protocols are often used to increase the network transmission rate and throughput. Although large-scale WSN can be supported by high bandwidth backbone network, the WSN remains the bottleneck due to resource constraints of wireless sensors and the effects of wireless interference. In this paper, we propose a multipath energy-efficient routing protocol for WSN that considers wireless interference. In the proposed routing protocol, nodes in the interference zone of the discovered path are marked and not allowed to take part in the subsequent routing process. In this way, the quality of wireless communication is improved because the effects of wireless interference can be reduced as much as possible. The network load is distributed on multiple paths instead of concentrating on only one path, and node energy cost is more balanced for the entire wireless network. The routing protocol is simulated in NS2 software. Simulation result shows that the proposed routing protocol achieves lower energy cost and longer network lifetime than that in the literature.

Maximizing Networking Capacity in Multi-Channel Multi-Radio Wireless Networks

Providing each node with one or more multi-channel radios offers a promising avenue for enhancing the network capacity by simultaneously exploiting multiple non-overlapping channels through different radio interfaces and mitigating interferences through proper channel assignment. However, it is quite challenging to effectively utilize multiple channels and/or multiple radios to maximize throughput capacity. The National Natural Science Foundation of China（NSFC） Project61128005 conducted comprehensive algorithmic-theoretic and queuing-theoretic studies of maximizing wireless networking capacity in multi-channel multi-radio（MC-MR） wireless networks under the protocol interference model and fundamentally advanced the state of the art. In addition, under the notoriously hard physical interference model, this project has taken initial algorithmic studies on maximizing the network capacity, with or without power control. We expect the new techniques and tools developed in this project will have wide applications in capacity planning, resource allocation and sharing, and protocol design for wireless networks, and will serve as the basis for future algorithm developments in wireless networks with advanced features, such as multi-input multi-output（MIMO） wireless networks.

Multipath Routing of Multiple Description Coded Images in Wireless Networks

Multiple description coding （MDC） generates multiple decodable bitstreams for a source to combat informa- tion loss. In this paper, multipath routing problem for two-description coded images is investigated for traditional and coded wireless networks without and with coding capability at intermediate nodes, respectively. Firstly, we formulate an interference-aware MDC multipath routing for traditional networks by employing a time-division link scheduling method to eliminate wireless interference, and ultimately obtain an optimal path selection corresponding to the minimum achievable distortion. Secondly, for coded networks, we evaluate practical wireless network coding （NC） in delivering descriptions of multiple unicast sessions. While NC increases maximum supporting flow rate of MDC descriptions in wireless networks, possible undecodability of NC mixed information is alleviated by MDC. To minimize achievable distortion, a proposed interference-and-coding-aware MDC multipath routing strikes a good balance between minimizing side effect of wireless interference avoidance and maximizing NC opportunity. Simulation results validate the effectiveness of the two proposed schemes.