Abstract-Supported by IEEE 802.15.4 standardization activities, embedded networks have been gaining in popularity in recent years. The focus of this paper is to quantify the behavior of key networking metrics of IEEE 802.15.4 beacon-enabled nodes under typical operating conditions, with the inclusion of packet retransmissions. We correct and extend previous analyses by scrutinizing the assumptions on which the prevalent Markovian modeling is generally based. By means of a comparative study, we single out which of the assumptions impact each of the performance metrics (throughput, delay, power consumption, collision probability, and packet discard probability). In particular, we show that-unlike what is usually assumed-the probability that a node senses the channel busy is not constant for all the stages of the backoff procedure and that these differences have a noticeable impact on backoff delay, packet discard probability, and power consumption. Similarly, we show that-again contrary to common assumption-the probability of obtaining transmission access to the channel depends on the number of nodes that are simultaneously sensing it. We evidence that ignoring this dependance has a significant impact on the calculated values of throughput and collision probability. Circumventing these and other assumptions, we rigorously characterize, through a semianalytical approach, the key metrics in a beacon-enabled IEEE 802.15.4 system with retransmissions.

Dynamic Channel Bonding (DCB) allows for the dynamic selection and use of multiple contiguous basic channels in Wireless Local Area Networks (WLANs). A WLAN operating under DCB can enjoy a larger bandwidth, when available, and therefore achieve a higher throughput. However, the use of larger bandwidths also increases the contention with adjacent WLANs, which can result in longer delays in accessing the channel and consequently, a lower throughput. In this paper, a scenario consisting of multiple WLANs using DCB and operating within carrier-sensing range of one another is considered. An analytical framework for evaluating the performance of such networks is presented. The analysis is carried out using a Markov chain model that characterizes the interactions between adjacent WLANs with overlapping channels. An algorithm is proposed for systematically constructing the Markov chain corresponding to any given scenario. The analytical model is then used to highlight and explain the key properties that differentiate DCB networks of WLANs from those operating on a single shared channel. Furthermore, the analysis is applied to networks of IEEE 802.11ac WLANs operating under DCB-which do not fully comply with some of the simplifying assumptions in our analysis-to show that the analytical model can give accurate results in more realistic scenarios.

Abstract-In this paper a candidate protocol to replace the prevalent CSMA/CA medium access control in Wireless Local Area Networks is presented. The proposed protocol can achieve higher throughput than CSMA/CA, while maintaining fairness, and without additional implementation complexity. Under certain circumstances, it is able to reach and maintain collision-free operation, even when the number of contenders is variable and potentially large. It is backward compatible, allowing for new and legacy stations to coexist without degrading one another's performance, a property that can make the adoption process by future versions of the standard smooth and inexpensive 1 .

This paper presents a new deterministic closed-loop phase-alignment algorithm based on quantized feedback from the receiver for distributed beamforming. In contrast with previously proposed methods, which entailed repeated transmissions from all the nodes in the network, this new algorithm requires each node to transmit only once during the synchronization cycle. This drastically reduces the amount of power consumed to achieve phase alignment, yet the new algorithm converges at least as fast as all other existing schemes. In contrast with previous analyses of distributed beamforming based on random phase updates, where noise had been disregarded, here it is explicitly included in the models and shown to have a considerable effect that cannot be ignored. With and without noise, analytical expressions that characterize the performance of the new algorithm are provided, with emphasis on various limiting regimes of interest.

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