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1. Multi-Objective Heuristics For Network Construction In An Obstacle-Dense Environment

   https://doi.org/10.1109/CASE59546.2024.10711579  

   Bernardini, Francesco; Becker, Aaron T (August 2024, IEEE)

   We present a heuristic method to construct an optimal communication network in an obstacle-dense environment. A set of immobile terminals must be connected by a network of straight-line edges by adding agents to serve as relays. Obstacles are represented by polygons, unaccessible by the agents of the network or by the edges. The problem with obstacles is reduced to a problem without obstacles by choosing the nodes of the optimal network among the obstacles’ vertices that are in mutual line of sight. A second heuristic method is developed to solve the bicriteria optimization problem with number of agents and length of the network as concurrent costs. 

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2. Joint Link-level and Network-level Reconfiguration for mmWave Backhaul Survivability in Urban Environments

   https://doi.org/10.1145/3345768.3355913  

   Liu, Yuchen; Hu, Qiang; Blough, Douglas M. (January 2019, Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems)

   mmWave communication has been recognized as a highly promising technology for 5G wireless backhaul, which is capable of providing multi-gigabit per second transmission rates. However, in urban wireless backhaul environments, unforeseen events can cause short-term blockages or node failures and, therefore, network survivability is extremely important. In this paper, we investigate a novel relay-assisted mmWave backhaul network architecture, where a number of small-cell BSs and relays are deployed, e.g. on the lampposts of urban streets. Relays are used to provide multi-hop line-of-sight paths between small-cell BSs, which form logical links of the network. In this scenario, the interconnected logical links make up a mesh network, which offers opportunities for both link-level and network-level reconfiguration. We propose two joint link-network level reconfiguration schemes for recovery after exceptional events. One prioritizes relay path (link-level) reconfiguration and uses alternate network-level paths only if necessary. The other splits traffic on both reconfigured links and backup paths to improve network throughput. Simulation results demonstrate that the proposed schemes significantly outperform purely link-level and purely network-level reconfiguration schemes. The proposed approaches are shown to not only maintain high network throughput but to also provide robust blockage/fault tolerance across a range of scenarios for urban mmWave backhaul networks. 

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3. Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

   https://doi.org/10.1109/TVT.2023.3274763  

   Kim, Dohyun; Castellanos, Miguel R.; Heath, Robert W. (October 2023, IEEE Transactions on Vehicular Technology)

   Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper address the question: how can the best relay be selected with minimal overhead from beam management? In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam in- dices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numeri- cal experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels. 

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4. Cognitive relay networks with energy and mutual-information accumulation

   https://doi.org/10.1109/INFCOMW.2018.8406845  

   Atat, Rachad; Ma, Junchao; Chen, Hao; Lee, Ulysses; Ashdown, Jonathan; Liu, Lingjia (April 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

   Cognitive radio networks, a.k.a. dynamic spectrum access networks, offer a promising solution to the problems of spectrum scarcity and under-utilization. In this paper, we consider two single-user links: primary and secondary links. To increase secondary user (SU) transmission opportunities and increase primary user (PU) throughput, we consider a cognitive relay network where a SU relays PU packets that are unsuccessfully received at the primary receiver (PR). At the PR side, two protocols are suggested: i) energy accumulation (EA), and ii) mutual-information accumulation (MIA). The average stable throughput of the secondary link is derived under these protocols for a specific throughput selected by the primary link. Results show that EA and MIA can significantly improve the secondary throughput compared with the no accumulation scenario, especially under extreme environment. 

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5. Vertex Sparsification for Edge Connectivity

   https://doi.org/10.1137/1.9781611976465.74  

   Chalermsook, Parinya; Das, Syamantak Das; Kook, Yunbum; Laekhanukit, Bundit; Liu, Yang P.; Peng, Richard Peng; Sellke, Mark; Vaz, Daniel (January 2021, Proceedings of the 2021 {ACM-SIAM} Symposium on Discrete Algorithms, SODA 2021, Virtual Conference, January 10 - 13, 2021)

   null (Ed.)

   Graph compression or sparsification is a basic information-theoretic and computational question. A major open problem in this research area is whether $$(1+\epsilon)$$-approximate cut-preserving vertex sparsifiers with size close to the number of terminals exist. As a step towards this goal, we initiate the study of a thresholded version of the problem: for a given parameter $$c$$, find a smaller graph, which we call \emph{connectivity-$$c$$ mimicking network}, which preserves connectivity among $$k$$ terminals exactly up to the value of $$c$$. We show that connectivity-$$c$$ mimicking networks of size $O(kc^4)$ exist and can be found in time $$m(c\log n)^{O(c)}$$. We also give a separate algorithm that constructs such graphs of size $$k \cdot O(c)^{2c}$$ in time $$mc^{O(c)}\log^{O(1)}n$$. These results lead to the first offline data structures for answering fully dynamic $$c$$-edge-connectivity queries for $$c \ge 4$$ in polylogarithmic time per query as well as more efficient algorithms for survivable network design on bounded treewidth graphs. 

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