An Optimal Power Allocation Scheme in Downlink Multi-user NOMA Beamforming System with Imperfect CSI

Liu, Xiaoguang; ZhangT, Jun; Cai, Shu

doi:10.1109/iccs.2018.8689239

Cited by 6 publications

(11 citation statements)

References 10 publications

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“…Bai et al [16] studied the fair resource allocation of device-to-device (D2D) communication underlaying cellular systems with CSI estimation error; they developed a joint resource scheduling scheme about resource chunk and power allocation among the D2D and cellular network users. Liu et al [17] proposed an optimal power allocation scheme for the downlink multi-user NOMA beamforming system with imperfect CSI. However, these works [13]- [17] do not consider the stochastic packet arrival characteristics and network stability (i.e., poor channel situation and/or low transmit power may lead to network congestion).…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [17] proposed an optimal power allocation scheme for the downlink multi-user NOMA beamforming system with imperfect CSI. However, these works [13]- [17] do not consider the stochastic packet arrival characteristics and network stability (i.e., poor channel situation and/or low transmit power may lead to network congestion). Liu et al [18] proposed a joint power and subchannel allocation policy for the downlink of an OFDMA system, with various practical considerations including stochastic packet arrivals, time-varying channels, and imperfect CSI, but the objective of their work was to minimize the total power consumption.…”

Section: Introductionmentioning

confidence: 99%

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Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI

Jiao

Dong

et al. 2020

IEEE Internet Things J.

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The Internet of Things (IoT) increases the number of connected devices and supports ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance computation capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels and imperfect estimation of channel state information (CSI). In this paper, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter β without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Optimal Resource Scheduling for Internet of Things Under Imperfect CSI

Jiao

Dong

et al. 2020

IEEE Internet Things J.

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“…In [6], the authors studied the robust beamforming design problem for NOMA in MISO channels by maximizing the worst-case achievable sum-rate. Not only is the knowledge of CSI an important factor affecting the performance of NOMA, the correlation between the user channels in each cluster is another crucial parameter [4,7]. In [4], the authors provided a user clustering method based on the correlation between the user channels of the MISO-NOMA system.…”

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confidence: 99%

“…It was shown that the higher the correlation between the channels of two users, the less the inter-cluster interference of the weak user. This work was extended to the imperfect CSI scenario with regularized ZF (RZF) precoding in [7].…”

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confidence: 99%

“…The main issue for the two power allocation schemes in [4,7] is that Monte-Carlo averaging over all channel realizations, which has high computational complexity, is needed. In this letter, we consider a downlink MISO-NOMA system with imperfect CSI using RZF precoding and aim to obtain a closedform expression of the optimal power allocation by maximizing an approximate expression of the achievable ergodic sumrate.…”

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Large System Analysis of Downlink MISO-NOMA System via Regularized Zero-Forcing Precoding With Imperfect CSIT

Zhu

et al. 2020

IEEE Commun. Lett.

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This letter studies the multiple-input single-output (MISO) non-orthogonal multiple-access (NOMA) downlink using regularized zero-forcing (RZF) precoding with imperfect channel state information (CSI). We first propose a new user scheduling scheme based on imperfect CSI and a model to characterize the channel correlation between the weak and strong users. Then we derive an approximate expression of the ergodic sum-rate using large-system random matrix theory. This approximation permits us to derive the optimal power allocation scheme that satisfies the rate requirement of the weak users. Simulation results are presented to confirm the accuracy of the approximation and reveal the relationships between the ergodic sum-rate, the channel correlation, and other system parameters. Index Terms-NOMA, imperfect CSI, regularized zero-forcing precoding, user clustering. I. INTRODUCTION Non-orthogonal multiple-access (NOMA) is regarded as the radio access technique to address the massive connectivity problem for Internet of Things (IoT) [1, 2]. With successive interference cancellation (SIC) at the receiver and superposition coding at the transmitter [3], NOMA has the capability to deliver greater performance, such as higher user fairness and improved spectrum efficiency, when compared to orthogonal multiple access (OMA) methods. User clustering, scheduling, power allocation, and multiuser beamforming are among the methods that can further improve the sum-rate and energy efficiency of the multiple-input singleoutput (MISO)-NOMA downlink [4, 5]. To reduce users' intercluster interferences, [4] applied zero forcing (ZF) precoding and clustering to MISO-NOMA. In [5], user clustering,

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