Abstract: The embodiments relate to modeling wireless communications in a network. In wireless communications, the nodes share one or more wireless communication channels. When a node has data to transmit, it must contend with the other nodes for access to the wireless communication channel. In the embodiments, the model is configured to simulate the throughput effects of contention including delays caused by retransmissions due to interference and collisions, listen-and-backoff, unavailability of time slots, etc. The occurrence of failed/dropped transmissions due to buffer overflows, excessive retransmission attempts, and unintended collisions are modeled as well. In addition, the embodiments may simulate the effect of mobility by the nodes and the effect of the location of the nodes relative to each other.

Abstract: The embodiments relate to modeling wireless communications in a network. In wireless communications, the nodes share one or more wireless communication channels. When a node has data to transmit, it must contend with the other nodes for access to the wireless communication channel. In the embodiments, the model is configured to simulate the throughput effects of contention including delays caused by retransmissions due to interference and collisions, listen-and-backoff, unavailability of time slots, etc. The occurrence of failed/dropped transmissions due to buffer overflows, excessive retransmission attempts, and unintended collisions are modeled as well. In addition, the embodiments may simulate the effect of mobility by the nodes and the effect of the location of the nodes relative to each other.

Abstract: Simulation models of media access control and physical layer characteristics facilitate the simulation/emulation of a variety of phenomena that affect transmissions via a wireless media. Such phenomena include media access contention delays, packet drops, and retransmissions that are generally dependent upon changes in transmitter/receiver locations. Each wireless environment is characterized by a model of the communication channel that characterizes transmission effects based on the number of competing transmitters in the environment, which is dynamically determined based on the location of each node in the environment. Additionally, the location of nodes is used to simulate the effects of ‘hidden nodes’, nodes that are unknown to a transmitting node but can interfere with the reception of transmissions at a receiving node. Each device/node model in the wireless environment preferably accesses the same model of the communication channel, thereby minimizing the amount of detail required at each device model.

Abstract: Channel access delays and reception uncertainty are modeled as protocol-independent generic processes that are optimized for improved simulation performance. The generic process components are designed such that each different protocol can be modeled using an arrangement of these components that is specific to the protocol. In this way, speed and/or accuracy improvements to the generic process components are reflected in each of such protocol models. If an accurate analytic model is not available for the generic process component, a prediction engine, such as a neural network, is preferably used. The prediction engine is trained using the existing detailed models of network devices. Once trained, the prediction engine is used to model the generic process, and the protocol model that includes the generic component is used in lieu of the detailed models, thereby saving substantial processing time.

Abstract: A transmission channel allocation scheme for a multi-hop wireless network takes into account the priority of users, particular application requirements, applicable contractual requirements, and other factors. The channel allocation scheme determines which nodes can share a common channel for transmission without interference, and distinguishes between resources that must be dedicated to each node based on the requirements associated with each node, and the resources that are dynamically provided to each node, based on the current traffic demand. Additionally, resources that are not required to satisfy explicit requests are allocated among the nodes, thereby allowing nodes to sometimes avoid the delays associated with the access request process.

Abstract: Channel access delays and reception uncertainty are modeled as protocol-independent generic processes that are optimized for improved simulation performance. The generic process components are designed such that each different protocol can be modeled using an arrangement of these components that is specific to the protocol. In this way, speed and/or accuracy improvements to the generic process components are reflected in each of such protocol models. If an accurate analytic model is not available for the generic process component, a prediction engine, such as a neural network, is preferably used. The prediction engine is trained using the existing detailed models of network devices. Once trained, the prediction engine is used to model the generic process, and the protocol model that includes the generic component is used in lieu of the detailed models, thereby saving substantial processing time.