532 research outputs found
Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks
Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix
Network-coded NOMA with antenna selection for the support of two heterogeneous groups of users
The combination of Non-Orthogonal Multiple Access (NOMA) and Transmit Antenna Selection (TAS) techniques
has recently attracted significant attention due to the low cost,
low complexity and high diversity gains. Meanwhile, Random
Linear Coding (RLC) is considered to be a promising technique
for achieving high reliability and low latency in multicast
communications. In this paper, we consider a downlink system
with a multi-antenna base station and two multicast groups of
single-antenna users, where one group can afford to be served
opportunistically, while the other group consists of comparatively
low power devices with limited processing capabilities that have
strict Quality of Service (QoS) requirements. In order to boost
reliability and satisfy the QoS requirements of the multicast
groups, we propose a cross-layer framework including NOMAbased TAS at the physical layer and RLC at the application
layer. In particular, two low complexity TAS protocols for NOMA
are studied in order to exploit the diversity gain and meet the
QoS requirements. In addition, RLC analysis aims to facilitate
heterogeneous users, such that, sliding window based sparse RLC
is employed for computational restricted users, and conventional
RLC is considered for others. Theoretical expressions that
characterize the performance of the proposed framework are
derived and verified through simulation results
Approximating optimal Broadcast in Wireless Mesh Networks with Machine Learning
With the growth of IoT, efficient broadcast is required for many applications. Yet, current protocols use primitive mechanisms based on heuristics. Multi-agent reinforcement learning is applied to approximate optimal broadcast in Wireless Mesh Networks. One of the proposed fully distributed algorithms, using Bayesian Neural Networks, outperforms MORE multicast and BATMAN, improving airtime up to 20%, e2e delay up to 30%, and satisfying timeout constraints in over the 97% of the cases
Network Coding-Based Next-Generation IoT for Industry 4.0
Industry 4.0 has become the main source of applications of the Internet of Things (IoT), which is generating new business opportunities. The use of cloud computing and artificial intelligence is also showing remarkable improvements in industrial operation, saving millions of dollars to manufacturers. The need for time-critical decision-making is evidencing a trade-off between latency and computation, urging Industrial IoT (IIoT) deployments to integrate fog nodes to perform early analytics. In this chapter, we review next-generation IIoT architectures, which aim to meet the requirements of industrial applications, such as low-latency and highly reliable communications. These architectures can be divided into IoT node, fog, and multicloud layers. We describe these three layers and compare their characteristics, providing also different use-cases of IIoT architectures. We introduce network coding (NC) as a solution to meet some of the requirements of next-generation communications. We review a variety of its approaches as well as different scenarios that improve their performance and reliability thanks to this technique. Then, we describe the communication process across the different levels of the architecture based on NC-based state-of-the-art works. Finally, we summarize the benefits and open challenges of combining IIoT architectures together with NC techniques
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