Throughput maximisation in cognitive radio networks with residual bandwidth

Paul, Anal; Banerjee, Avik; Maity, Santi P.

doi:10.1049/iet-com.2018.5928

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…The noise at the

i th

SU receiver follows circularly symmetric complex Gaussian random variable with zero mean and variance

bold-italicE false[| υ false(n false) |^{2} false] = P_{n}

. The wireless channel is modelled as Rayleigh flat fading, where a fading coefficient is

h_{i} \sim C N false(0, d_{i}^{- α} false)

[19]. It is assumed that FC contains K number of antennae, such as

{AT}_{1}

{AT}_{2}

,…,

{AT}_{K}

.…”

Section: System Modelmentioning

confidence: 99%

Spectrum sensing in cognitive vehicular networks for uniform mobility model

Paul¹,

Kunarapu²,

Banerjee³

et al. 2019

IET Communications

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“…The noise at the

i th

SU receiver follows circularly symmetric complex Gaussian random variable with zero mean and variance

bold-italicE false[| υ false(n false) |^{2} false] = P_{n}

. The wireless channel is modelled as Rayleigh flat fading, where a fading coefficient is

h_{i} \sim C N false(0, d_{i}^{- α} false)

[19]. It is assumed that FC contains K number of antennae, such as

{AT}_{1}

{AT}_{2}

,…,

{AT}_{K}

.…”

Section: System Modelmentioning

confidence: 99%

Spectrum sensing in cognitive vehicular networks for uniform mobility model

Paul¹,

Kunarapu²,

Banerjee³

et al. 2019

IET Communications

Self Cite

View full text Add to dashboard Cite

“…The RRHs include a high power node (HPN) which transmits high power to the users and several lower power nodes (LPNs) which accommodate fewer users than HPN. A centralized signal processing is used in BBU pool which reduces the manufacturing and operating cost [4], [5]. However, enormous data can be transmitted over backhaul links.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation and EE-SE Tradeoff for H-CRAN with NOMA-Based D2D Communications

2020

KSII TIIS

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We propose a general framework for studying resource allocation problem and the tradeoff between spectral efficiency (SE) and energy efficiency (EE) for downlink traffic in power domain-non-orthogonal multiple access (PD-NOMA) and device to device (D2D) based heterogeneous cloud radio access networks (H-CRANs) under imperfect channel state information (CSI). The aim is jointly optimize radio remote head (RRH) selection, spectrum allocation and power control, which is formulated as a multi-objective optimization (MOO) problem that can be solved with weighted Tchebycheff method. We propose a low-complexity algorithm to solve user association, spectrum allocation and power coordination separately. We first compute the CSI for RRHs. Then we study allocating the cell users (CUs) and D2D groups to different subchannels by constructing a bipartite graph and Hungrarian algorithm. To solve the power control and EE-SE tradeoff problems, we decompose the target function into two subproblems. Then, we utilize successive convex program approach to lower the computational complexity. Moreover, we use Lagrangian method and KKT conditions to find the global optimum with low complexity, and get a fast convergence by subgradient method. Numerical simulation results demonstrate that by using PD-NOMA technique and H-CRAN with D2D communications, the system gets good EE-SE tradeoff performance.

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Utility Aware Cooperative Spectrum Sharing in Overlay Cognitive Radio Networks

Sharma

Sarma²

2022

Int J Wireless Inf Networks

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Throughput maximisation in cognitive radio networks with residual bandwidth

Cited by 7 publications

References 20 publications

Spectrum sensing in cognitive vehicular networks for uniform mobility model

Spectrum sensing in cognitive vehicular networks for uniform mobility model

Resource Allocation and EE-SE Tradeoff for H-CRAN with NOMA-Based D2D Communications

Utility Aware Cooperative Spectrum Sharing in Overlay Cognitive Radio Networks

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