[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84693-en":3,"doc-seo-84693-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84693,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Scale-Free Beamforming using Swarm Arrays for Remote Sensing under Interference","A swarm array of autonomous relays cooperatively forwards a desired signal to a fusion center while canceling multiple co-channel interferers. A distributed algorithm computes optimal zero-forcing beamforming weights at the relays without prior channel knowledge. The method is scale-free: computational and bandwidth overheads do not depend on array size, enabled by the Collective Array constraint where relays communicate only through aggregate external signals. The resulting beamforming supports robustness to noise and channel time variations.","Scale-Free Beamforming using Swarm Arrays for Remote Sensing under Interference  \n1st Bradley Hamilton Electrical & Computer Engineering University of Iowa Iowa City, USA email address or ORCID  \n2nd Raghu Mudumbai Electrical & Computer Engineering University of Iowa Iowa City, USA email address or ORCID  \n3rd Soura Dasgupta Electrical & Computer Engineering University of Iowa Iowa City, USA email address or ORCID  \narXiv :2607 .03499v 1 [ cs .DC] 3 Jul 2026  \n4th Benjamin Peiffer Applied Research and Technology  \nCollins Aerospace  \nCedar Rapids, USA [benjamin.peiffer@collins.com](benjamin.peiffer@collins.com)  \nAbstract—We consider a swarm array of autonomous relays that seek to cooperatively forward a desired signal to a fusion center with the maximum possible fidelity while canceling out a number of interferers. We present a distributed algorithm for computing the optimal zero-forcing beamforming weights at therelays without requiring prior channel knowledge. Crucially, our algorithm is scale-free in the sense that the computational and bandwidth overheads are completely independent of the size of the array. We build on recent work that introduced the concept of a Collective Array that enables such scale-free computation by imposing a constraint that the array must always function as a swarm i.e. array elements can only ever communicate with external nodes collectively and never individually. While this is a very severe restriction, we show that it allows useful computations such as zero-forcing beamforming while being robust to noise and channel time-variations.  \nIndex Terms—scale-free arrays, cooperative relays, distributed beamforming, subspace projection, least mean squares  \nI. INTRODUCTION  \nWe consider the problem of optimally isolating a desired signal from multiple co-channel interfering signals using a distributed sensor array. We propose a novel scale-free method for solving this problem under which the computational and bandwidth overheads of optimal beamforming and interference cancellation are entirely independent of the size of the sensor array. This potentially allows massive scaling of such arrays and their deployment in situations where it is necessary to operate autonomously with limited coordination. In our conception, the sensor array is deployed as a network of amplify-and-retransmit relays to construct a collaborative beamformer as shown in Fig. 1. Calculating the optimal amplification weights requires accounting for a cascaded, twohop channel model: the sensing channel from the sources to the relay array, and the reachback channel from the relay array to the final destination (fusion center) . The goal of sensor fusion can then be formalized as the problem of computing a complex weight vector that maximizes the received SNR  \nThis work is partially supported by NSF grant 2540120 .  \nof the desired source at the fusion center while enforcing spatial nulls (zero-forcing) against all known interferers. Our main contribution is a distributed, scale-free algorithm for adaptively computing these optimal weights in the sensor array.  \nA. The Scale-Free Swarm Array  \nWe leverage the Collective Array concept introduced in [1] . The Collective Array is defined by the constraint that all array processing must be completely scale-free in the sense that the computational and bandwidth requirements are entirely independent of the array size. This constraint is guaranteed by requiring that all outward communication from the array must only be in the form of aggregated signals i.e. combinations of signals from all individual sensors, while incoming communication is always addressed to the array asa whole and never to any individual element. All external entities only send and receive messages to the array as a collective, which in effect, renders individual elements to be entirely invisible to all external nodes and even to each other.  \nThis Collective Array concept ensures scale-free operation, but represents an","cbCaikBhHiwMIR7o","https://ap.wps.com/l/cbCaikBhHiwMIR7o","pdf",477417,2,1,6,"English","en",105,"# Introduction\n## The Scale-Free Swarm Array\n## Contributions\n## Background and Related Work","[{\"question\":\"What problem does the document address?\",\"answer\":\"It addresses isolating a desired signal from multiple co-channel interferers using a distributed sensor array with amplify-and-retransmit relays toward a fusion center.\"},{\"question\":\"How are the optimal beamforming weights obtained?\",\"answer\":\"A distributed scale-free algorithm adaptively computes the optimal zero-forcing beamforming weights at the relays without requiring prior channel state information.\"},{\"question\":\"Why is the approach called “scale-free”?\",\"answer\":\"Because computational and bandwidth overheads are independent of the array size, enforced by the Collective Array constraint that only aggregated signals can be exchanged with external 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