The blocking performance of wavelength routing WDM optical networks can be improved by employing wavelength conversion. In this paper, we address the problem of optimally placing a limited number of wavelength converters in mesh topologies. Two objective functions, namely, minimizing the average blocking probability and minimizing the maximum blocking probability over all routes, are considered. We Brst extend an earlier analytical model to compute the blocking probability on an arbitrary route in a mesh topology, given the trafflc and locations of converters. We then propose heuristic algorithms t o place wavelength converters, and evaluate the performance of the proposed heuristics using the analytical model.

This paper considers the channel-assignment and scheduling in wireless mesh networks that employ multiple radios and multiple channels. In contrast to the various algorithms available in the literature, we explicitly model the delay overhead that is incurred during channel switching, and use that delay in the design of algorithms. We prove that the well known Greedy Maximal Scheduling (GMS) algorithm does not have any provable efficiency ratio when the switching overhead is considered. We present a centralized algorithm (CGSSO), and a dynamic algorithm (DMSSO), both of which consider switching overhead. Simulation results show that the proposed algorithms significantly outperform other algorithms in packet throughput and average packet delay metrics. Results also show that the improvements in performance become more pronounced as the switching delay increases.

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Abstract-Channel-aware scheduling and link adaptation methods are widely considered to be crucial for realizing high data rates in wireless networks. However, predicting the future channel states, and adjusting the transmission schedules and parameters accordingly, may consume valuable system resources, such as bandwidth, time, and power. This paper considers the trade-offs between the prediction quality and the throughput in a wireless network that uses link adaptation and channel-aware scheduling. In particular, we study the effects of the look-ahead window, i.e., the range of future time slots on which we have channel state estimates, and the reliability of the channel state estimates on the throughput. We develop an online scheduling algorithm for a multi-channel multi-user network that employs predictive link adaptation, and generalize it to incorporate imperfect channel state estimates. We use this heuristic together with performance bounds to the offline version of the problem to evaluate the performance with varying prediction quality. Our results suggest that it may be possible to reap most of the potential channel-aware scheduling benefits with a small look-ahead, and imperfect channel state estimates. Thus, a modest consumption of resources for channel prediction and link adaptation may result in a significant throughput improvement, with only marginal gains through further enhancement of the prediction quality. Our results can provide meaningful guidelines in deciding what level of system resource consumption is justified for channel quality estimation and link adaptation. I. MOTIVATIONThis paper considers the downlink scheduling problem in a multi-carrier wireless network that uses link adaptation. Link Adaptation (LA), which loosely refers to changing transmission parameters over a link, such as modulation, coding rate, power, etc., in response to changing channel conditions is considered to be a powerful means of achieving higher efficiency or throughput in wireless networks. The adaptation of the transmission parameters is performed according to the predicted future quality of the channel, also called as the channel state (CS).While it is desirable to adapt the transmission parameters according to the channel state information (CSI) to capture even small-scale variations, there are practical limitations to channel state prediction and link adaptation. Frequent adaptation increases the number of mode-change messages transmitted over the channel, consuming bandwidth, and time resources [1]. While many aspects of scheduling transmissions over time-varying wireless channels have been studied (see, for example, [2], [3], [4] and the references therein), the penalty induced by LA has not been considered. Moreover, predicting the future channel quality may also consume significant amount of system resources (e.g., time, bandwidth and power), since it may involve transmission of training-sequences, pilot tones, or feedback messages carrying the CSI. Naturally, the additional cost and complication of pred...