Search arXivSearch

arXiv · 2012.01228

MirrorVLC: Optimal Mirror Placement for Multi-Element VLC Networks

Abstract

Visible Light Communication (VLC) is a rapidly growing technology which can supplement the current radio frequency (RF) based wireless communication systems. VLC can play a huge part in solving the ever-increasing problem of spectrum scarcity because of the growing availability of Light Emitting Diodes (LEDs). One of the biggest advantages of VLC over other communication systems is that it can provide illumination and data communication simultaneously without needing any extra deployment. Although it is essential to provide data rate at a blazing speed to all the users nowadays, maintaining a satisfactory level in the distribution of lighting is also important. In this paper, we present a novel approach of using mirrors to enhance the illumination uniformity and throughput of an indoor multi-element VLC system architecture. In this approach, we improve the Signal-to-Interference plus Noise Ratio (SINR) of the system and overall illumination uniformity of the room by redirecting the reflected LED beams on the walls to darker spots with the use of mirrors. We formulate a joint optimization problem focusing on maximization of the SINR while maintaining a reasonable illumination uniformity across the room. We propose a two-stage solution of the optimization problem with optimization of illumination in the first stage and SINR at the second stage. We propose three different heuristic solutions for the second stage and analyze the performance of them, which demonstrates the advantages of each heuristic for different possible scenarios. We also show that about threefold increase in average illumination and fourfold increase in average throughput can be achieved when the mirror placement is applied which is a significant performance improvement.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sifat Ibne Mushfique, Ahmad Alsharoa, Murat Yuksel. 2020-12-02. MirrorVLC: Optimal Mirror Placement for Multi-Element VLC Networks. https://arxiv.org/abs/2012.01228

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

KEEP EXPLORING

Related papers

Semord: Learned Semantic-Preserving Placement and Low-Fanout Routing for Distributed Vector Search

Vector databases are increasingly deployed in distributed settings where different users, sites, or domains maintain vector data. Existing vector databases rely on a coordinator to record which shards store which parts of the vector space and to route each query to those shards. In a decentralized setting, peers may join, leave, or move data without a trusted node tracking every change, and outdated routing information can therefore send queries to the wrong peers or require contacting many peers, reducing vector retrieval recall and increasing network latency. We present Semord, a decentralized vector search overlay system that achieves high recall by routing each ANN query to a small set of relevant peers, without relying on a centralized coordinator. Semord addresses this problem by making semantic locality routable: 1) We propose VHash to place semantically related vectors near each other in the overlay key space while avoiding load imbalance, so that each query only needs to contact a small neighborhood of peers for distributed local ANN ranking. 2) We design VecDHT, a communication protocol that maintains decentralized routing, region metadata, churn resilience, and VHash updates under membership and workload changes. Our extensive experiments on a real testbed show that Semord improves recall by more than 15% and reduces contacted peers by over 60% compared with decentralized baselines. Semord also approaches the recall and latency of a centralized oracle baseline while reducing peak peer-local ANN index memory by more than 2X. Controlled large-scale simulations further show that Semord scales across real-world embedding workloads and remains robust under churn for scoped vector retrieval as a decentralized overlay.

cs.NI

Lizard: Bandwidth-Adaptive Real-Time Video Analytics through Content-Aware Packet Discarding at Last-Mile Edge Routers

The timeliness and accuracy of edge-based video analytics can be hindered by drastic reductions in available bandwidth (ABW) at last-mile edge routers, causing prolonged queuing delays. This work proposes Lizard, a system that leverages video-content-aware packet discarding to mitigate the negative effects of drastic ABW degradation that may frequently occur at a last-mile edge router by judiciously discarding packets that contain frame blocks less important to the analytics at the destination. To achieve this, we first devise a frame-block-aware RTP header extension to effectively decouple packet dependencies to encode frame blocks. Second, Lizard uses a priority-based feedback mechanism that dynamically evaluates packet priorities based on relative accuracy impacts. Third, we develop an adaptive phase-transition-based packet discarding strategy at the router to discard packets that represent unimportant blocks. Our evaluation of Lizard shows improvements over existing methods are substantial: 53.2% reduction in latency and 27.1% increase in analysis accuracy.

cs.NI

Flux: Optimal Scheduling of Optical Circuit Switches for LLM Training

Optical Circuit Switching (OCS) offers high bandwidth density and energy efficiency for LLM training, but incurs a non-negligible reconfiguration delay. Prior work typically schedules optical circuit switches independently of compute, using aggregate traffic demand to determine which circuits to provision and when. We argue that this separation creates a fundamental inefficiency: reconfigurations that ignore the compute timeline can stall communication, resulting in low circuit utilization and large buffer requirements. In this paper, we present Flux, a scheduler that optimally schedules optical circuit switches based on the structure of the entire workload. Flux remains effective across a wide range of switching speeds by reusing circuits and amortizing reconfiguration delay behind compute and communication. We show that Flux reduces training iteration time by up to $10\times$ and peak NIC buffer requirements by more than three orders of magnitude compared to traditional periodic schedulers.

cs.NI