Search arXiv⌕ Search

arXiv · 1401.7313

Deterministic Blind Rendezvous in Cognitive Radio Networks

Abstract

Blind rendezvous is a fundamental problem in cognitive radio networks. The problem involves a collection of agents (radios) that wish to discover each other in the blind setting where there is no shared infrastructure and they initially have no knowledge of each other. Time is divided into discrete slots; spectrum is divided into discrete channels, $\{1,2,..., n\}$. Each agent may access a single channel in a single time slot and we say that two agents rendezvous when they access the same channel in the same time slot. The model is asymmetric: each agent $A_i$ may only use a particular subset $S_i$ of the channels and different agents may have access to different subsets of channels. The goal is to design deterministic channel hopping schedules for each agent so as to guarantee rendezvous between any pair of agents with overlapping channel sets. Two independent sets of authors, Shin et al. and Lin et al., gave the first constructions guaranteeing asynchronous blind rendezvous in $O(n^2)$ and $O(n^3)$ time, respectively. We present a substantially improved construction guaranteeing that any two agents, $A_i$, $A_j$, will rendezvous in $O(|S_i| |S_j| \log\log n)$ time. Our results are the first that achieve nontrivial dependence on $|S_i|$, the size of the set of available channels. This allows us, for example, to save roughly a quadratic factor over the best previous results in the important case when channel subsets have constant size. We also achieve the best possible bound of $O(1)$ time for the symmetric situation; previous works could do no better than $O(n)$. Using the probabilistic method and Ramsey theory we provide evidence in support of our suspicion that our construction is asymptotically optimal for small size channel subsets: we show both a $c |S_i||S_j|$ lower bound and a $c \log\log n$ lower bound when $|S_i|, |S_j| \leq n/2$.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sixia Chen, Alexander Russell, Abhishek Samanta, Ravi Sundaram. 2014-01-28. Deterministic Blind Rendezvous in Cognitive Radio Networks. https://arxiv.org/abs/1401.7313

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Secure Polarization-Shift Backscatter Identification Applied to Battery-Free BLE Sensors Powered by Wireless Power Transfer

This paper presents a lightweight and protocolindependent security mechanism for battery-free Bluetooth Low Energy (BLE) sensor nodes operating in Simultaneous Wireless Information and Power Transfer (SWIPT) architecture. The proposed approach exploits polarization-shift backscattering of the wireless power wave to transmit an encrypted device identification prior to data communication. A fail-safe RF switch and orthogonally polarized antennas are integrated as an external add-on module, enabling controlled backscatter without modifying the original energy-harvesting rectifier. The identification payload is encrypted using AES-128 and transmitted with minimal energy overhead. Experimental validation on a battery-free BLE sensor node demonstrates reliable extraction of the backscattered identification signal, seamless coexistence with BLE advertising, and improved RF-to-DC harvesting efficiency compared to rectifier-based backscatter solutions. The results confirm that polarization-shift backscatter identification provides an effective and practical security for battery-free BLE sensing systems.

cs.NI↗

From WPT to Encrypted Telemetry: A Battery-Free Backscattering-based Polarimetric Wireless Sensor

This work introduces an indoor Battery-Free Wireless Sensing Node powered through radiative Wireless Power Transfer (WPT). The proposed platform targets secure, energyefficient active sensing and overcomes key limitations of many prior battery-free approaches, which commonly provide neither on-node computation nor cryptographic protection. The node combines temperature, humidity, pressure and Volatile Organic Compound (VOC) measurements with a low-power microcontroller that executes sensor calibration, derives a VOC index, formats the payload, and applies AES-128 encryption before wireless transmission. Energy harvesting and communication are enabled by a 1-bit controlled Backscatter Rectenna (BR), which both scavenges incident RF power and produces an orthogonally polarized backscattered signal for robust polarimetric operation. Experimental results validate reliable multi-sensor readout and encrypted data transfer, while maintaining a very low energy budget for the complete sense-compute-encrypt-transmit cycle.

cs.NI↗

NebulaSD: Many-for-Many Speculative Decoding

Speculative decoding accelerates Large Language Model (LLM) inference by using a lightweight draft model to propose candidate tokens for parallel verification by a target model. Drafting and verification, however, exhibit different service characteristics and favor different batch configurations, making fixed draft-target coupling inefficient under concurrent workloads. Existing distributed designs can physically separate the two stages, but often retain request or batch affinities that prevent their capacities from being shared globally. We present NebulaSD, a many-for-many, or M-for-N, speculative decoding system that organizes draft and target workers into independently schedulable resource pools and dynamically reconstructs stage-specific batches from shared request pools. Such dynamic reassignment removes fixed worker locality, requiring request states to be made available at newly selected workers without introducing migration stalls. NebulaSD addresses this challenge through worker-triggered batch reconstruction and asynchronous KV-state preparation overlapped with model execution. We evaluate NebulaSD from both system and scaling perspectives, showing that dynamic pooling improves request-round processing rate by 50.4% over a physically disaggregated baseline and 72.6% over co-located execution on a four-GPU deployment while substantially increasing effective GPU utilization. Profile-driven simulations further show approximately proportional compute-side capacity scaling under idealized state movement.

cs.NI↗