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1. Load Balanced Controller Association in Wireless Distributed SDNs

   https://doi.org/10.1109/PerComWorkshops48775.2020.9156178  

   Starke, Allen; McNair, Janise (March 2020, IEEE Pervasive Computing Conference PhD Workshop)

   null (Ed.)

   Wireless infrastructure is steadily evolving into wireless access for all humans and most devices, from 5G to Internet-of-Things. This widespread access creates the expectation of custom and adaptive services from the personal network to the backbone network. In addition, challenges of scale and interoperability exist across networks, applications and services, requiring an effective wireless network management infrastructure. For this reason Software-Defined Networks (SDN) have become an attractive research area for wireless and mobile systems. SDN can respond to sporadic topology issues such as dropped packets, message latency, and/or conflicting resource management, to improved collaboration between mobile access points, reduced interference and increased security options. Until recently, the main focus on wireless SDN has been a more centralized approach, which has issues with scalability, fault tolerance, and security. In this work, we propose a state of the art WAM-SDN system for large-scale network management. We discuss requirements for large scale wireless distributed WAM-SDN and provide preliminary benchmarking and performance analysis based on our hybrid distributed and decentralized architecture. Keywords: software defined networks, controller optimization, resilience. 

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2. AoI, Timely-Throughput, and Beyond: A Theory of Second-Order Wireless Network Optimization

   https://doi.org/10.1109/TNET.2024.3432655  

   Guo, Daojing; Nakhleh, Khaled; Hou, I-Hong; Kompella, Sastry; Kam, Clement (December 2024, IEEE/ACM Transactions on Networking)

   This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which only consider first-order statistics, this framework models every random process by its mean and temporal variance. The inclusion of temporal variance makes this framework well-suited for modeling Markovian fading wireless channels and emerging network performance metrics such as age-of-information (AoI) and timely-throughput. Using this framework, we sharply characterize the second-order capacity region of wireless access networks. We also propose a simple scheduling policy and prove that it can achieve every interior point in the second-order capacity region. To demonstrate the utility of this framework, we apply it to an unsolved network optimization problem where some clients wish to minimize AoI while others wish to maximize timely-throughput. We show that this framework accurately characterizes AoI and timely-throughput. Moreover, it leads to a tractable scheduling policy that outperforms other existing work. 

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3. Deep correlated speckles: suppressing correlation fluctuation and optical diffraction

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   Nie, Xiaoyu; Song, Haotian; Ren, Wenhan; Zhang, Zhedong; Peng, Tao; Scully, Marlan O. (April 2024, Photonics Research)

   The generation of speckle patterns via random matrices, statistical definitions, or apertures may not always result in optimal outcomes. Issues such as correlation fluctuations in low ensemble numbers and diffraction in long-distance propagation can arise. Instead of improving results of specific applications, our solution is catching deep correlations of patterns with the framework, Speckle-Net, which is fundamental and universally applicable to various systems. We demonstrate this in computational ghost imaging (CGI) and structured illumination microscopy (SIM). In CGI with extremely low ensemble number, it customizes correlation width and minimizes correlation fluctuations in illuminating patterns to achieve higher-quality images. It also creates non-Rayleigh nondiffracting speckle patterns only through a phase mask modulation, which overcomes the power loss in the traditional ring-aperture method. Our approach provides new insights into the nontrivial speckle patterns and has great potential for a variety of applications including dynamic SIM, X-ray and photo-acoustic imaging, and disorder physics. 

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4. RENEW: Programmable and Observable Massive MIMO Networks

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   Doost-Mohammady, Rahman; Bejarano, Oscar; Zhong, Lin; Cavallaro, Joseph R.; Knightly, Edward; Mao, Z. Morley; Li, Wei Wayne; Chen, Xuemin; Sabharwal, Ashutosh (October 2018, 2018 IEEE 52nd Asilomar Conference on Signals, Systems, and Computers)

   Massive MIMO is one of the key technologies in 5G wireless broadband, capable of delivering substantial improvements in capacity of next-generation wireless networks. However, due to its inherent complexity, its operation, reconfiguration, and enhancement present significant challenges and risks. In this paper we present RENEW, a fully programmable and observable massive MIMO network. We present the architectural design for full programmability at every layer of the wireless stack, from the radio hardware, including PHY and MAC layer configurations, all the way up to the network core functionality using network function virtualization. We also present mechanisms to enable observability at every layer of the stack. These include various indicators in the radio and core access network, hence enabling effective monitoring, troubleshooting, and performance evaluation of the network at large. 

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5. On the extraction of RF fingerprints from LSTM hidden-state values for robust open-set detection

   https://doi.org/10.52953/MOGL1293  

   Puppo, Luke; Wong, Weng-Keen; Hamdaoui, Bechir; Elmaghbub, Abdurrahman; Lin, Lucy (January 2024, ITU Journal on Future and Evolving Technologies)

   New capabilities in wireless network security have been enabled by deep learning that leverages and exploits signal patterns and characteristics in Radio Frequency (RF) data captured by radio receivers to identify and authenticate radio transmitters. Open-set detection is an area of deep learning that aims to identify RF data samples captured from new devices during deployment (aka inference) that were not part of the training set; i.e. devices that were unseen during training. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. In this paper, we introduce a novel open-set detection approach for RF data-driven device identification that extracts its neural network features from patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory (CNN+LSTM) model. Experimental results obtained using real datasets collected from 15 IoT devices, each enabled with LoRa, wireless-Wi-Fi, and wired-Wi-Fi communication protocols, show that our new approach greatly improves the area under the precision-recall curve, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices. 

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