Power controlled multiple access (PCMA) in wireless communication networks

Bambos, Nicholas; Kandukuri, S.

doi:10.1109/infcom.2000.832211

Cited by 69 publications

(59 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bambos and Kandukuri [8] is concerned with power control for packetized data. They show that the transmitter matches interference for low values of the interference, and as the interference increases the transmitter backs off; backlog concerns make the receiver more aggressive.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic power control in a fading downlink channel subject to an energy constraint

Ata

Zachariadis

2006

Queueing Syst

View full text Add to dashboard Cite

A central controller chooses a state-dependent transmission rate for each user in a fading, downlink channel by varying transmission power over time. For each user, the state of the channel evolves over time according to an exogenous continuous-time Markov chain (CTMC), which affects the quality of transmission. The traffic for each user, arriving at the central controller, is modeled as a finite-buffer Markovian queue with adjustable service rates. That is, for each user data packets arrive to the central controller according to a Poisson process and packet size is exponentially distributed; an arriving packet is dropped if the associated buffer is full, which results in degradation of quality of service. The controller forwards (downlink) the arriving packets to the corresponding user according to an optimally chosen transmission rate from a fixed set A i of available values for each user i, depending on the backlog in the system and the channel state of all users. The objective is to maximize quality of service subject to an upper bound on the longrun average power consumption. We show that the optimal transmission rate for each user is solely a function of his own packet queue length and channel state; the dependence among users is captured through a penalty rate. Further, we explicitly characterize the optimal transmission rate for each user.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Dynamic power control in a fading downlink channel subject to an energy constraint

Ata

Zachariadis

2006

Queueing Syst

View full text Add to dashboard Cite

show abstract

“…In [5] and [6], the transmit power and cost incurred in the buffer are used as the objective functions to be minimized. In [13], the number of information bits successfully transmitted per Joule is used as the objective function.…”

Section: A Reward Functionmentioning

confidence: 99%

“…We first briefly outline the existing wireless resource management approaches, which are closer to our method. In [5], a power control scheme for wireless packet networks is formulated using dynamic programming (DP). The extension of this work to multimodal power control is also investigated in [6].…”

mentioning

confidence: 99%

Near-optimal reinforcement learning framework for energy-aware sensor communications

Pandana

Liu

2005

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Abstract-We consider the problem of average throughput maximization per total consumed energy in packetized sensor communications. Our study results in a near-optimal transmission strategy that chooses the optimal modulation level and transmit power while adapting to the incoming traffic rate, buffer condition, and the channel condition. We investigate the point-to-point and multinode communication scenarios. Many solutions of the previous works require the state transition probability, which may be hard to obtain in a practical situation. Therefore, we are motivated to propose and utilize a class of learning algorithms [called reinforcement learning (RL)] to obtain the near-optimal policy in point-to-point communication and a good transmission strategy in multinode scenario. For comparison purpose, we develop the stochastic models to obtain the optimal strategy in the point-to-point communication. We show that the learned policy is close to the optimal policy. We further extend the algorithm to solve the optimization problem in a multinode scenario by independent learning. We compare the learned policy to a simple policy, where the agent chooses the highest possible modulation and selects the transmit power that achieves a predefined signal-to-interference ratio (SIR) given one particular modulation. The proposed learning algorithm achieves more than twice the throughput per energy compared with the simple policy, particularly, in high packet arrival regime. Beside the good performance, the RL algorithm results in a simple, systematic, self-organized, and distributed way to decide the transmission strategy.Index Terms-Energy-aware sensor communications, Markov decision process (MDP), reinforcement learning (RL).

show abstract

“…This in turn depends on the transmission conditions, the distance of the destination and finally the number of bits in the packet. The exact dependence has been studied extensively in [2], and [5]. In general, when the transmission conditions are poor, or the next hop node is farther away, or the packet has a larger number of bits, the packet needs to be transmitted at higher power and as such it has a larger size.…”

Section: System Assumptions and Objectivesmentioning

confidence: 99%

A framework for optimal battery management for wireless nodes

Sarkar

Adamou

2003

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

The focus of this paper is to extend the lifetime of a battery powered node in wireless context. The lifetime of a battery depends on both the manner of discharge and the transmission power requirements. We present a framework for computing the optimal discharge strategy which maximizes the lifetime of a node by exploiting the battery characteristics and adapting to the varying power requirements for wireless operations. The complexity of the optimal computation is linear in the number of system states. However, since the number of states can be large, the optimal strategy can only be computed offline and executed via a table lookup. We present a simple discharge strategy which can be executed online without any This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract-The focus of this paper is to extend the lifetime of a battery powered node in wireless context. The lifetime of a battery depends on both the manner of discharge and the transmission power requirements. We present a framework for computing the optimal discharge strategy which maximizes the lifetime of a node by exploiting the battery characteristics and adapting to the varying power requirements for wireless operations. The complexity of the optimal computation is linear in the number of system states. However, since the number of states can be large, the optimal strategy can only be computed offline and executed via a table lookup. We present a simple discharge strategy which can be executed online without any table lookup and attains near maximum lifetime.

show abstract

Power controlled multiple access (PCMA) in wireless communication networks

Cited by 69 publications

References 10 publications

Dynamic power control in a fading downlink channel subject to an energy constraint

Dynamic power control in a fading downlink channel subject to an energy constraint

Near-optimal reinforcement learning framework for energy-aware sensor communications

A framework for optimal battery management for wireless nodes

Contact Info

Product

Resources

About