## Distributed opportunistic scheduling for ad hoc communications with imperfect channel information,” Submitted to

Venue: | V. CONCLUSION In |

Citations: | 23 - 8 self |

### BibTeX

@INPROCEEDINGS{Zheng_distributedopportunistic,

author = {Dong Zheng and Man-on Pun and Weiyan Ge and Junshan Zhang and H. Vincent Poor},

title = {Distributed opportunistic scheduling for ad hoc communications with imperfect channel information,” Submitted to},

booktitle = {V. CONCLUSION In},

year = {}

}

### OpenURL

### Abstract

Abstract — Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation, where the transmission rate needs to be backed off from the estimated rate to reduce the outage. It is shown that the optimal scheduling policy remains to be threshold-based, and that the rate threshold turns out to be a function of the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, a suboptimal linear backoff scheme that backs off the estimated signal-to-noise ratio (SNR) and hence the rate is proposed. The corresponding optimal backoff ratio and rate threshold can be obtained via an iterative algorithm. Finally, simulation results are provided to illustrate the tradeoff caused by increasing training time to improve channel estimation at the cost of probing efficiency. I.

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Citation Context ...el aware random access has been investigated for the uplink transmissions in a many-to-one network, where channel probing can be realized by broadcasting pilot signals from the base station. Notably, =-=[9]-=-, [10] study opportunistic ALOHA under a collision model, with a basic idea being that in every slot each user transmits with a probability based on its own channel condition. While recent work [11] d... |

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Citation Context ...ergence of {x(t)} by appealing to the mean ordinary differential equation (ODE) method. The convergence of the original sequence {x(k)} then follows by using the similar argument as in Section 1.3 in =-=[37]-=-. Towards this end, define Dm(k) � [xm(k) + am(k)�gm(x(k))I{n ∈ N m }] b 0 − [xm(k) + am(k)�gm(x)I{k ∈ N m }]. Sm(k) � gm(x(k)). (75) (33) can be rewritten as xm(k + 1) = xm(k) + Dm(k) + am(k)Sm(k)I{k... |

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Citation Context ...wireless networks (see, e.g., [23], [24], [25], [26]). We note that a game theoretic formulation on random access protocols has been investigated, and the most relevant to our study are perhaps [27], =-=[28]-=-, [29], with one major difference being that none of these works exploit time-varying channel conditions for scheduling. The rest of the paper is organized as follows. Section II gives a brief introdu... |

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Citation Context ... great interest to study DOS under noisy channel estimation. In centralized scheduling schemes, it has been shown that the estimated signal-to-noise ratio (SNR) is always larger than the “actual SNR” =-=[6]-=-. Thus, if the data were transmitted using the estimated rate, there would always be an outage. To reduce the outage probability, the transmission rate has to back off from the estimated rate. Therefo... |

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Citation Context ...are random access has been investigated for the uplink transmissions in a many-to-one network, where channel probing can be realized by broadcasting pilot signals from the base station. Notably, [9], =-=[10]-=- study opportunistic ALOHA under a collision model, with a basic idea being that in every slot each user transmits with a probability based on its own channel condition. While recent work [11] does no... |

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Citation Context ...y ∈ [0, b]. (37) This lemma follows from the fact that the derivative of g(·) is bounded. 3) Stochastic Convergence of the Algorithm: Define the stepsize as k� ai(k) = a(i, I{k ∈ N i }). l=1 Based on =-=[32]-=-, [19], we impose the following conditions. B1) The sequence {a(i, k)} satisfy ∞� ∞� a(i, k) = ∞, and a(i, k) 2 < ∞. k=1 and for β ∈ (0, 1), ∀ i, j lim k→∞ �⌊βk⌋ l=1 �k l=1 a(i, l) k=1 a(i, l) = 1, an... |

2 |
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Citation Context ...arate link losses caused by fading from those by interference. That is, the PHY layer addresses the issues of fading, and the MAC layer addresses the issue of contention. However, as shown in [1] and =-=[2]-=-, fading can often adversely affect the MAC layer in many realistic scenarios. The coupling between the time scales of fading and MAC calls for a unified PHY/MAC design for wireless ad hoc networks, i... |