NOMA-Enabled Optimization Framework for Next-Generation Small-Cell IoV Networks Under Imperfect SIC Decoding

Khan, Wali Ullah; Li, Xingwang; Ihsan, Asim; Khan, Mohammad Ayoub; Menon, Varun G.; Ahmed, Manzoor

doi:10.1109/tits.2021.3091402

Cited by 45 publications

(13 citation statements)

References 42 publications

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“…We assume that the energy of the channel with the DFT-D-LS method [40] is almost zero/negligible within the smearing area. 2 The channel energy of h(m) (corresponds to the IDFT output of H(k) in ( 11)) would be smeared uniformly over all channel paths, and first few taps contain maximum channel energy [27]. Therefore, these channel taps contain only estimation noise in the energy smearing region.…”

Section: B Transform Domain Channel Estimation With Dft-based D-ls (Dft-d-ls) Methodsmentioning

confidence: 99%

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

et al. 2021

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Channel estimation is a challenging and timely issue for the 3rd Generation Partnership Project (3GPP) standardized low power wide-area network technology named narrowband Internet of Things (NB-IoT). Channel estimation is crucial to achieve extended radio coverage, energy efficiency, coherent detection, and channel equalization for signal repetition dominated NB-IoT uplink transmission. The NB-IoT inherits simplified baseband radio frequency processing, physical channels, reference signal structure, and numerology from existing Long Term Evolution (LTE) systems to save power and costs. Thus, channel estimation methods extensively employed in LTE systems may not be applied to the NB-IoT uplink systems. In this paper, efficient discrete cosine transform type-I (DCT-I)-based transform-domain channel estimation approaches are proposed by modifying the original definition of DCT-I. The proposed methods can mitigate the problems experienced in the discrete Fourier transform (DFT)-based channel estimation, such as signal aliasing error, and border effect. The proposed approaches improve channel estimation precision by reducing signal distortion from the high-frequency region in the time-domain when non-sample-spaced path delays exist in multipath fading channels. Signal aliasing error experienced from the virtual subcarriers is also minimized with the anticipated schemes. The proposed methods are applied on simple least squares (LS) estimates in time-domain to eliminate estimation noise. The viability of the proposed estimators is verified as compared to the conventional LS, DFT-based de-noising LS, and standard DCT-I based methods through extensive numerical simulations. Based on the numerical simulations, the proposed estimators show better mean square error and bit error rate performances than their competitors in extremely low coverage conditions. INDEX TERMSChannel estimation, Internet of Things (IoT), Long Term Evolution (LTE), narrowband Internet of Things (NB-IoT), orthogonal frequency-division multiplexing (OFDM), signal repetition I. INTRODUCTION N ARROWBAND Internet of Things (NB-IoT) is an emerging and extremely narrowband cellular-based low power wide-area network (LPWAN) technology [1]-[5], which was introduced by the 3rd Generation Partnership Project (3GPP) as a representative technology in release-13 for providing massive connectivity of geographically distributed IoT devices to the Internet. The NB-IoT will be a strategic technology in the 5th generation (5G) new radio networks, and a crucial driver in future semiconductor

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Section: B Transform Domain Channel Estimation With Dft-based D-ls (Dft-d-ls) Methodsmentioning

confidence: 99%

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

et al. 2021

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“…Pw,e,r,P re,w,r,ηw,e,r L(P w,e,r , P r e,w,r , η w,e,r , x w,e,r , µ w , µ e , w,e,r , τ r , λ, λ w ), where L is called the Lagrangian and is given in (25) on the 2 A discussion on the convexity of the problem is provided in the appendix. 3 Duality theory based solution provide convergence to a local maxima [45].…”

Section: P2: Maxmentioning

confidence: 99%

“…The exponentially growing number of connected vehicles has brought an unconventional change in sixth generation (6G) intelligent transportation systems [1], [2]. The aim of advance transportation systems is to support massive vehicles connections and high transmission rate.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Resource Allocation for 6G NOMA-Enabled Cooperative Vehicular Networks

Ali

Khan

Ihsan

et al. 2021

IEEE Open J. Intell. Transp. Syst.

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In recent years, the concept of non-orthogonal multiple access (NOMA) has gathered much attention due to its potential to offer high spectral efficiency, present user fairness and grant free access to sixth generation (6G) vehicular networks. This paper proposes a new optimization framework for NOMAenabled cooperative vehicular network. In particular, we jointly optimize the vehicle paring, channel assignment, and power allocation at source and relaying vehicles. The objective is to maximize the sum rate of the system subject to the power allocation, minimum rate, relay battery lifetime and successive interference cancellation constraints. To solve the joint optimization problem efficiently, we adopt duality theory followed by Karush-Kuhn-Tucker (KKT) conditions, where the dual variables are iteratively computed through sub-gradient method. Two less complex suboptimal schemes are also presented as the benchmark cooperative vehicular schemes. Simulation results compare the performance of the proposed joint optimization scheme compared to the other benchmark cooperative vehicular schemes.

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“…The details of proposed algorithm AOBWS is summarized in Algorithm 1. Once RSU received the information from RSs, then that information can be provided to the vehicles through energy efficient schemes proposed in [31], [32].…”

Section: B Efficient Selection Of Rc For Rsmentioning

confidence: 99%

Energy-Efficient Backscatter Aided Uplink NOMA Roadside Sensor Communications under Channel Estimation Errors

Ihsan¹,

Chen²,

Khan³

et al. 2021

Preprint

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This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients (RCs) of backscatter aided roadside sensors (RSs) are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closedform computed through Cardano's formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal RC for RSs under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.

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NOMA-Enabled Optimization Framework for Next-Generation Small-Cell IoV Networks Under Imperfect SIC Decoding

Cited by 45 publications

References 42 publications

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

Optimizing Resource Allocation for 6G NOMA-Enabled Cooperative Vehicular Networks

Energy-Efficient Backscatter Aided Uplink NOMA Roadside Sensor Communications under Channel Estimation Errors

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