Packet Switching in Radio Channels: Part ICarrier Sense MultipleAccess Modes and Their ThroughputDelay Characteristics

Cognitive radios (CR) technology enables wireless devices to dynamically switch between channels in a wide range of frequency bands and utilize unused parts of licensed and unlicensed frequency bands. The CR-enabled devices can either form a stand-alone multi-hop wireless network or form a multi-hop wireless backbone network that can connect different types of devices together. In a multi-hop wireless network composed of CR-enabled devices the number and the set of available channels vary from one node to another. This is in contrast to the existing multi-hop wireless mesh networks where all the nodes have the same set of channels available. In this paper we present a CSMA-based MAC protocol for CR-based multihop network. This protocol is based on the channel segregation technique proposed for cellular networks. Simulation results indicate that the protocol incurs low latency and achieves reasonable throughput.

CSMA-Based MAC Protocol For Cognitive Radio Networks

Cognitive Radio (CR) exploits underutilized licensed spectrums to improve its bandwidth availability. Using CR technology, a node is able to adapt its transmission and reception radio parameters including channel frequency dynamically according to local spectrum availability. For channel access between wireless nodes, a cognitive Medium Access Control (MAC) protocol is necessary to coordinate the CRs. Multi-channel MAC protocol extensions have been proposed in IEEE802.11 to enable a node to operate in multiple channels in order to improve network-wide throughput. These multi-channel MAC protocols have several functions that can be leveraged by cognitive MAC protocols due to their similarities in certain aspects, though the CR has an additional requirement to cope with the existence of licensed users that have higher authority over the channels. Current research in cognitive MAC protocols assumes the availability of a common control channel at all times, which is an approach in the multi-channel MAC protocols. This approach has certain hardware requirements that may not be readily available at CR nodes. Hence, other approached may be necessary. In this paper, various types of multi-channel MAC protocols are reviewed, followed by discussion of their merits and demerits in multi-channel environments. The purpose is to show the additional functionalities and challenges that each multi-channel MAC protocol has to offer and address in order to operate in multihop CR networks. By providing discussion on possible technology leverage from multi-channel to cognitive MAC protocols, we aim to establish a foundation for further research and discussion.

On Multi-Channel MAC Protocols in Cognitive Radio Networks

Partial differential equations (PDEs) see widespread use in sciences and engineering to describe simulation of physical processes as scalar and vector fields interacting and coevolving over time. Due to the computationally expensive nature of their standard solution methods, neural PDE surrogates have become an active research topic to accelerate these simulations. However, current methods do not explicitly take into account the relationship between different fields and their internal components, which are often correlated. Viewing the time evolution of such correlated fields through the lens of multivector fields allows us to overcome these limitations. Multivector fields consist of scalar, vector, as well as higher-order components, such as bivectors and trivectors. Their algebraic properties, such as multiplication, addition and other arithmetic operations can be described by Clifford algebras. To our knowledge, this paper presents the first usage of such multivector representations together with Clifford convolutions and Clifford Fourier transforms in the context of deep learning. The resulting Clifford neural layers are universally applicable and will find direct use in the areas of fluid dynamics, weather forecasting, and the modeling of physical systems in general. We empirically evaluate the benefit of Clifford neural layers by replacing convolution and Fourier operations in common neural PDE surrogates by their Clifford counterparts on 2D Navier-Stokes and weather modeling tasks, as well as 3D Maxwell equations. For similar parameter count, Clifford neural layers consistently improve generalization capabilities of the tested neural PDE surrogates. Source code for our PyTorch implementation is available at https://microsoft.github.io/cliffordlayers/.

Clifford Neural Layers for PDE Modeling

Increasing the communication capacity of a shared ad-hoc wireless channel by using MUD to distinguish overlapping information transmitted simultaneously by a plurality of nodes. The transmitting nodes simultaneously provide parameter-estimating signals over separate, unshared, low-rate parameter channels generated using orthogonal frequencies, spread spectrum technology, or time multiplexing. Receiving nodes use these separate, non-overlapping parameter-estimating signals to estimate MUD-required signal parameters such as amplitude, phase, and frequency offset, thereby enabling use of lower complexity MUD receivers, because the parameters are not estimated in the presence of other interference. Node ID, spreading code type, and/or other information can also be transmitted over the parameter channels. Limiting the number of parameter channels can limit the maximum number of transmitting nodes. Amplitudes of parameter channel transmissions can be greater than communication channel transmissions by a known ratio. Parameter channels can be frequency-hopped for jam-resistance.

Media access control protocol for multiuser detection enabled ad-hoc wireless communications

This paper proposes a new multi-channel MAC Protocol with Power Control for Ad hoc Networks. First, the code channel is divided into common channel, broadcast channel and data channel while code synchronization is dynamically implemented through RTS/CTS handshake. In order to decrease handshake cost and avoid code channel switching frequently, Dual Reservation scheme is proposed through ACK Piggybacking. And furthermore, dynamic power control mechanism is implemented to reduce near-far interference. Besides, broadcast is well supported by an out-of-band busy tone. Compared with IEEE 802.11 DCF protocol, the simulation results show that the proposed mechanism can achieve better performance of network throughput.

A New Multi-channel MAC Protocol with Power Control for Ad hoc Networks

Traffic data prediction offers a significant way to evaluate the future traffic congestion status; many deep learning based approaches have been widely applied in this field. Most current methods only consider short-term traffic data forecasting; however, long-term prediction, which supports the optimized distribution of traffic resources, is not well studied. Besides, multiple traffic parameters enable stronger constraints for the data estimation, but the correlation between them in both spatial and temporal domains has not been efficiently learned. Geometric algebra, as a generalization of linear algebra, provides a framework to encode multidimensional data and analyze the correlation. By combining the advantages of the deep neural network and geometric algebra, a multi-channel geometric algebra residual network (MGAResNet) is proposed to address the problem of long-term traffic data prediction. Traffic data obtained from two urban expressways are employed and experimental results demonstrate that the approach outperforms the state-of-the-art work. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/itr2.12232

A multi‐channel geometric algebra residual network for traffic data prediction

A new multi-channel MAC Protocol with Power Control for Ad hoc Networks

Communication countermeasure combat effectiveness evaluation is related to the adjustment of combat means and system command coordination. As qualitative description of communication countermeasure combat effectiveness evaluation is difficult to accurately to evaluate, Combination method, which combines objective evaluation and subjective evaluation effectively is adopted, and the weight distribution of the evaluation index is more scientific and reasonable. The cloud model is used to evaluate the operational effectiveness of communication countermeasures, the forward cloud generator can transform the qualitative evaluation results into quantitative computable values, and then the final operational effectiveness evaluation results can be obtained by the reverse cloud generator. The results show that this method can integrate subjective and objective information well, effectively integrate fuzziness and randomness, and has strong operability. It can provide an effective method for evaluating the effectiveness of communication countermeasures.

Xihao Chen

Papers

A New Multi-channel MAC Protocol with Power Control for Ad hoc Networks

A multi‐channel geometric algebra residual network for traffic data prediction

A new multi-channel MAC Protocol with Power Control for Ad hoc Networks

Evaluation Method of Communication Countermeasure Combat Effectiveness based on Cloud Model