Distributed power allocation algorithm in wireless networks under SNR constraints

Campos-Delgado, Daniel U.; Rivera, Juan Pablo

doi:10.1016/j.aeue.2013.06.002

Cited by 9 publications

(5 citation statements)

References 40 publications

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“…This study showed that the best compromise is achieved by the LQG control in [22]. On the other hand, the results in [28] show that this power control scheme presents the optimal solution to improve robustness to quantization and measurement noise in wireless networks. These previous studies highlight that the LQG control is the best choice for the inner-loop structure.…”

Section: B Utility Optimizationmentioning

confidence: 84%

“…Thus, the inner-loops are responsible to guarantee the desired QoS in the V2I communication link despite channel variations, and network latency. The state of the art in distributed power control algorithms for wireless networks is vast [6], [22], [28], [29]. In [29], the robustness to round-trip delay uncertainty, and reference tracking performance was studied for seven power control schemes: (i) Fixed-step, (ii) Foschini-Miljanic, (iii) proportional-integral-derivative control, (iv) H ∞ robust control, (v) Robust Smith predictor, (vi) Variable structure control, and (vii) linear-quadratic-gaussian (LQG) control.…”

Section: B Utility Optimizationmentioning

confidence: 99%

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Distributed Utility Optimization in Vehicular Communication Systems

Diaz-Ibarra

Campos-Delgado

Gutiérrez

et al. 2020

IEEE Trans. Veh. Technol.

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In this paper, we study the problem of utility maximization in the uplink of vehicle-to-infrastructure communication systems. The studied scenarios consider four practical aspects of mobile radio communication links: i) Interference between adjacent channels, ii) interference between roadside units along the way, iii) fast and slow channel fadings, and iv) Doppler shift effects. We present first the system model for the IEEE 802.11p standard, which considers a communication network between vehicles and roadside infrastructure. Next, we formulate the problem of utility maximization in the network, and propose a distributed optimization scheme. This distributed scheme is based on a two-loop feedback configuration, where an outer-loop establishes the optimal signal to interference-noise ratio (SINR) that maximizes the utility function per vehicle and defines a quality-of-service objective. Meanwhile, inner-control loops adjust the transmission power to achieve this optimal SINR reference in each vehicle node regardless of interference, time-varying channel profiles and network latency. The computation complexity of the distributed utility maximization scheme is analyzed for each feedback loop. Simulation results indicate that the proposed scheme reaches the objective SINRs that maximize utility and improve energy efficiency in the network with a low time cost. The results also show that the maximum utility is consistently achieved for different propagation scenarios inside the vehicular communication network. Index Terms-Vehicular communications, transmission power, utility maximization, feedback control. I. INTRODUCTION A. Motivation V EHICULAR communication networks (VCN) are an emerging technology aimed at improving road safety and traffic management [1]-[3]. These networks will enable vehicles to share their driving information with other vehicles, and also with the infrastructure installed along the road in real-time. Two types of messages will be employed to transmit (broadcast) such Manuscript

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Section: B Utility Optimizationmentioning

confidence: 84%

Section: B Utility Optimizationmentioning

confidence: 99%

Distributed Utility Optimization in Vehicular Communication Systems

Diaz-Ibarra

Campos-Delgado

Gutiérrez

et al. 2020

IEEE Trans. Veh. Technol.

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“…Recently, the problems of power control under the effect of additive random noise due to quantization in the feedback path and signal-to-noise ratio limitations have been addressed in [4] and [27]. However, in practice, the quantization process of the feedback signals can have logarithmic or non-uniform patterns [10], [26] and floating-point representations [20], resulting in multiplicative stochastic models to characterize the induced uncertainty in the feedback systems.…”

Section: Introductionmentioning

confidence: 99%

Analysis of distributed power allocation in wireless networks under multiplicative noise and interference uncertainty

Campos-Delgado

Rivera

Rojas

2014

2014 American Control Conference

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In this paper, the problem of power allocation in a wireless network is studied with a distributed perspective by taking into account multiple-access interference in the communication channel, non-uniform quantization and floatingpoint operations. These three effects can be characterized by multiplicative uncertainty models in the forward and feedback paths of a closed-loop power control strategy. The quality of service (QoS) in the wireless data transmission is evaluated with respect to signal to interference-noise ratio (SINR) after the estimation process, where the uplink channel (active users to base station) is addressed by its implications with respect to battery consumption. The multiplicative uncertainty terms are modeled by a stochastic framework, and the mean-square small gain theorem is employed to evaluate robust stability. Two inequalities are then derived to check internal stability with respect to the norms of the multiplicative uncertainty terms, and sensitivity and complementary sensitivity transfer functions of the closed-loop system. As a case study we considered an LQG controller in each active user for power allocation, whose structure allows to derive simplified inequalities to evaluate robust stability.

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“…Efficient utilisation of the transmission power is fundamental since this resource affects directly the battery management in the mobile units (MUs), and the induced interference in the wireless network. Hence, in the literature, we find many proposals to regulate the power allocation at the transmission stage by open or closed‐loop schemes through, for example, passivity‐based control [1], game theory [2], linear quadratic control [3] or H ∞ optimal control [4]. In a closed‐loop methodology, there is feedback information that conditions the transmission power, which is usually related to the estimated QoS in the communication link.…”

Section: Introductionmentioning

confidence: 99%

“…In this way, the wireless network could re‐assign these resources to other services. The general allocation scheme is based on the traditional periodic transmission power control previously proposed in [3, 30]. Inspired by Heemels et al [22], we suggest to send the feedback information to the MUs only when there is a significant change in the QoS error signals, which is evaluated by a threshold value.…”

Section: Introductionmentioning

confidence: 99%