Topology-Aware Space-Time Network Coding in Cellular Networks

Torrea-Duran, Rodolfo; Morales-Céspedes, Máximo; Plata-Chaves, Jorge; Vandendorpe, Luc; Moonen, Marc

doi:10.1109/access.2017.2773709

Cited by 8 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…networks [3], cellular and radio access networks [4], [5], vehicular and wireless sensor networks [6], [7], and general wireless networks [8]- [12], as well as unreliable complex information technology infrastructures, such as data caching infrastructures [13]- [15]. One main hurdle that prevents the widespread adoption of RLNC in communication networks and information technology systems is the computationally highly demanding matrix multiplication and matrix inversion required for RLNC decoding [16]- [19].…”

Section: Random Linear Network Coding (Rlnc) Has the Potential To Grementioning

confidence: 99%

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

View full text Add to dashboard Cite

Fulcrum coding combines a high-field outer Random Linear Network Coding (RLNC) that generates outer coding expansion packets with a small-field inner RLNC that combines the source packets and the outer coding expansion packets. This two-layer Fulcrum coding allows flexible decoding in receivers with heterogeneous computational capabilities. Fulcrum coding has so far only been studied for conventional dense RLNC, which randomly selects all coding coefficients, and only for a statically fixed number of outer expansion packets. However, the probability that the coding coefficient row of a newly received packet is linearly independent of prior received coding coefficient rows (a prerequisite for successful decoding) is highly dynamic. We propose to exploit the dynamics of this probability to reduce the computational complexity of Fulcrum coding. In particular, we vary the density of non-zero coding coefficients, i.e., equivalently, the sparsity of coding coefficients, and the number of outer expansion packets to keep the complexity low while maintaining a reasonably high decoding probability. We introduce the general principles of dynamic sparsity and expansion packets (DSEP) for Fulcrum coding as well as two specific example DSEP policies. Our evaluations indicate that DSEP Fulcrum can increase the encoding throughput tenfold and increase the decoding throughput 1.4 to 4.3 fold while achieving decoding probabilities that are typically less than 1% lower than the conventional Fulcrum decoding probabilities. We also find that DSEP achieves somewhat higher encoding and decoding throughputs than the CodornicesRq (Release 2.1) implementation of RaptorQ block coding for small blocks (generations) of source packets, while RaptorQ is substantially faster for large generation sizes. Furthermore, we develop and evaluate an elementary DSEP recoding mechanism that achieves a recoding throughput more than double the decoding throughput.

show abstract

Section: Random Linear Network Coding (Rlnc) Has the Potential To Grementioning

confidence: 99%

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Understanding the network topology can prominently facilitate the design of efficient cellular networks, and pervasive applications depending upon the network topology have been found in the wireless communication field. For instance, the knowledge of network topology can be exploited to perform automatic computations for topology control in WSNs [15], investigate the pros and cons of BS switching on/off scheme [16], develop energy-saving mechanism for green cellular networks [17], improve the spectral efficiency and reduce the bit error rate (BER) in space-time network coding (STNC) scheme [18], manage the interference and allocate spectrum resource for a femtocell-based cellular network [19] and so on.…”

Section: A Related Workmentioning

confidence: 99%

Study on Base Station Topology in National Cellular Networks: Take Advantage of Alpha Shapes, Betti Numbers, and Euler Characteristics

Chen

Zhao

et al. 2020

IEEE Systems Journal

View full text Add to dashboard Cite

Faced with the ever-increasing trend of the cellular network scale, how to quantitatively evaluate the effectiveness of the large-scale deployment of base stations (BSs) has become a challenging topic. To this end, a deeper understanding of the cellular network topology is of fundamental significance to be achieved. In this paper, α-Shape, a powerful algebraic geometric tool, is integrated into the analysis of real BS location data for six Asian countries and six European countries, respectively. Firstly, the BS spatial deployments of both Asian and European countries express fractal features based on two different testifying metrics, namely the Betti numbers and the Hurst coefficients. Secondly, it is found out that the log-normal distribution presents the best match to the cellular network topology when the practical BS deployment is characterized by the Euler characteristics.

show abstract

“…Random linear network coding (RLNC) can significantly enhance the communication over unreliable complex networks, such as body area networks [1], caching networks [2]- [4], cellular networks [5], the Internet of Things (IoT) [6]- [8], radio access networks [9], vehicular networks [10], wireless sensor networks [11], and general wireless networks [12]- [16]. One main challenge of RLNC based communication is that the decoding in receiver nodes involves computationally highly demanding matrix multiplication and matrix inversion [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Progressive Multicore RLNC Decoding With Online DAG Scheduling

2019

View full text Add to dashboard Cite

A complete generation of packets coded with Random Linear Network Coding (RLNC) can be quickly decoded on a multicore system by scheduling the involved matrix block operations in parallel with an offline (pre-recorded) directed acyclic graph (DAG). The waiting for a complete generation of packets can be avoided with progressive RLNC decoding that commences the decoding (and can decode some packets) before all packets in a generation have been received. This article develops and evaluates a novel progressive RLNC decoding strategy based on the principle of DAG scheduling of parallel matrix block operations. The novel strategy involves helper matrices for conducting the Gauss Jordan elimination based on rows of blocks of matrix elements. The matrix block computations are dynamically scheduled by an online DAG which permits branching, e.g., to skip unnecessary matrix block operations. The throughput and delay of the novel progressive RLNC decoding strategy are evaluated with experiments on two heterogeneous multicore processor boards. The novel progressive RLNC decoding achieves throughput levels on par with stateof-the-art non-progressive (full-generation) RLNC decoding and achieves three times higher throughput than the fastest (highest-throughput) known progressive RLNC decoder for small generation sizes and short data packets. Also, our progressive RLNC decoding greatly reduces receiver delays for moderate to large generation sizes; the delay reductions are particularly pronounced when a low-delay RLNC version is employed (e.g., reduction to one tenth of the non-progressive decoding delay for a generation size of 256 packets). INDEX TERMS Directed acyclic graph (DAG), helper matrix, heterogeneous multicore architecture, online scheduling, parallel computing, random linear network coding (RLNC).

show abstract

Topology-Aware Space-Time Network Coding in Cellular Networks

Cited by 8 publications

References 17 publications

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

DSEP Fulcrum: Dynamic Sparsity and Expansion Packets for Fulcrum Network Coding

Study on Base Station Topology in National Cellular Networks: Take Advantage of Alpha Shapes, Betti Numbers, and Euler Characteristics

Progressive Multicore RLNC Decoding With Online DAG Scheduling

Contact Info

Product

Resources

About