Opportunistic Feedback Protocol for Minimizing Power in Uplink with Heterogeneous Traffic

Agarwal, Rajiv; Abhishek, Vishal; Vannithamby, Rath; Cioffi, J.M.

doi:10.1109/vetecf.2007.396

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Capacity analysis of the broadcast channel with feedback is analyzed in [8]. Opportunistic feedback approaches for designing uplink user transmission policies are discussed in [7]. An excellent overview of feedback and the compression of feedback in multiuser wireless systems is available in [86].…”

Section: A Single Antenna Scheduling and Spectrum Sharingmentioning

confidence: 99%

An overview of limited feedback in wireless communication systems

Love

Heath

Lau

et al. 2008

IEEE J. Select. Areas Commun.

1,354

918

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Abstract-It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

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Section: A Single Antenna Scheduling and Spectrum Sharingmentioning

confidence: 99%

An overview of limited feedback in wireless communication systems

Love

Heath

Lau

et al. 2008

IEEE J. Select. Areas Commun.

1,354

918

View full text Add to dashboard Cite

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“…Opportunistic power control (eg. [15], [16]) exploit channel variations to improve performance and fairness. In the recent years, tools from game theory have been used to address many variants of the power control problem (eg.…”

Section: Introductionmentioning

confidence: 99%

Satisfying demands in a multicellular network: A universal power allocation algorithm

Ramanath

Kavitha

Debbah

2011

2011 International Symposium of Modeling and Optimization of Mobile, Ad Hoc, and Wireless Networks

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Power allocation to satisfy user demands in the presence of large number of interferers in a multicellular network is a challenging task. Further, the power to be allocated depends upon the system architecture, for example upon components like coding, modulation, transmit precoder, rate allocation algorithms, available knowledge of the interfering channels, etc. This calls for an algorithm via which each base station in the network can simultaneously allocate power in a decentralized way to their respective users so as to meet their demands (when they are within the achievable limits), using whatever information is available of the other users. The goal of our research is to propose one such algorithm which in fact is universal: the proposed algorithm works from a fully co-operative setting to almost no co-operation (becomes completely decentralized) and or for any configuration of modulation, rate allocation, etc. schemes. The algorithm asymptotically satisfies the user demands, running simultaneously and independently within a given total power budget at each base station. Further, it requires minimal information to achieve this: every base station needs to know its own users demands, its total power constraint and the transmission rates allocated to its users in every time slot. We formulate the power allocation problem in a system specific game theoretic setting, define system specific capacity region and analyze the proposed algorithm using ordinary differential equation (ODE) framework. Simulations confirm the effectiveness of the proposed algorithm.

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