The demands for data rate and transmission reliability constantly increase with the explosive use of wireless devices and the advancement of mobile computing techniques. Multiple-input and multiple-output (MIMO) technique is considered as one of the most promising wireless technologies that can significantly improve transmission capacity and reliability. Many emerging mobile wireless applications require peer-to-peer transmissions over an ad hoc network, where the nodes often have a different number of antennas, and the channel condition and network topology vary over time. It is important and challenging to develop efficient schemes to coordinate transmission resource sharing among a heterogeneous group of nodes over an infrastructure-free mobile ad hoc network. In this work, we propose a holistic scheduling algorithm that can adaptively select different transmission strategies based on the node types and channel conditions to effectively relieve the bottleneck effect caused by nodes with smaller antenna arrays, and avoid the transmission failure due to the violation of lower degree of freedom constraint resulted from the channel dependency. The algorithm also takes advantage of channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes and provide special transmission support for higher priority nodes with weak channels, so that the data rate of the network can be maximized while user transmission quality requirement is supported. The performance of our algorithm is studied through extensive simulations and the results demonstrate that our algorithm is very effective in handling node heterogeneity and channel constraint, and can significantly increase the throughput while reducing the transmission delay.

Two fast growing technologies, MIMO and cognitive radio (CR), can both effectively combat the transmission interference among links and thus increase the network throughput. MIMO exploits spatial degree of freedom (DoF) through spatial multiplexing and interference cancellation within the same frequency channel, while CR exploits all available frequency channels for transmissions. We consider an ad hoc network where each node is equipped with an array of cognitive radios. A radio can tune to a different channel and transmit independently, or transmit together with other radios on the same channel using MIMO mode. Additionally, different frequency and spatial channels could have different conditions. There is a big challenge for nodes to distributively coordinate in selecting a transmission channel and/or a spatial DoF taking advantage of this unprecedented flexibility and diversity of channels for a higher network performance. In this work, we mathematically model the opportunities and constraints for such a network with the objective of maximizing the weighted network throughput. We propose a centralized algorithm as our comparison benchmark, and a distributed algorithm to flexibly assign spectrum channel or spatial DoF exploiting the multiuser diversity, channel diversity and spatial diversity for a higher performance in a practical network. The algorithm further supports different transmission priorities, reduces transmission delay and ensures fair transmissions among nodes by providing all nodes with certain transmission probability. The performance of our algorithms are studied through extensive simulations and the results demonstrate that our algorithm is very effective and can significantly increase the network throughput while reducing the delay.

With the popularity of wireless devices and the increase of computing and storage resources, there are increasing interests in supporting mobile computing techniques. Particularly, ad hoc networks can potentially connect different wireless devices to enable more powerful wireless applications and mobile computing capabilities. To meet the ever increasing communication need, it is important to improve the network throughput while guaranteeing transmission reliability. Multiple-input-multiple-output (MIMO) technology can provide significantly higher data rate in ad hoc networks where nodes are equipped with multiantenna arrays. Although MIMO technique itself can support diversity transmission when channel condition degrades, the use of diversity transmission often compromises the multiplexing gain and is also not enough to deal with extremely weak channel. Instead, in this work, we exploit the use of cooperative relay transmission (which is often used in a single antenna environment to improve reliability) in a MIMO-based ad hoc network to cope with harsh channel condition. We design both centralized and distributed scheduling algorithms to support adaptive use of cooperative relay transmission when the direct transmission cannot be successfully performed. Our algorithm effectively exploits the cooperative multiplexing gain and cooperative diversity gain to achieve higher data rate and higher reliability under various channel conditions. Our scheduling scheme can efficiently invoke relay transmission without introducing significant signaling overhead as conventional relay schemes, and seamlessly integrate relay transmission with multiplexed MIMO transmission. We also design a MAC protocol to implement the distributed algorithm. Our performance results demonstrate that the use of cooperative relay in a MIMO framework could bring in a significant throughput improvement in all the scenarios studied, with the variation of node density, link failure ratio, packet arrival rate, and retransmission threshold.

In recent years, we have witnessed a surge of interest in enabling communications over meshed wireless networks. Particularly, supporting peer-to-peer communications over a multihop wireless network has great potential in enabling ubiquitous computing. However, many wireless nodes have limited capabilities, for example, sensor nodes or small handheld devices. Also, the end-to-end capacity and delay degrade significantly as the path length increases with the number of network nodes. In these scenarios, the deployment of a backbone network could potentially facilitate higher performance network communications. In this paper, we study the novel reinforced backbone network (RBN) deployment problem considering the practical limitation in the number of available backbone nodes and enforcing backbone network connectivity. We propose an iterative and adaptive (ITA) algorithm for efficient backbone network deployment. In addition, in order to provide the performance bound, we redefine and solve the problem by implementing the genetic algorithm. Finally, we present our simulation results under various settings and compare the performance of the proposed ITA algorithm and the genetic algorithm. Our study indicates that the proposed ITA algorithm is promising for deploying a connected RBN with a limited number of available backbone nodes.

In multi-hop wireless networks where links are prone to be broken or degraded, it is important to guarantee the network connectivity as well as satisfy the performance requirements. Observing the promising features of Multiple-Input Multiple-Output (MIMO) techniques for improving the transmission capacity and reliability, in this paper, we make the very first attempt to deploy MIMO nodes as relays to assist weak links in wireless networks, with the aim of reducing the number of relay nodes and providing performance provisioning. We identify the specific constraints of MIMO relay nodes for assisting weak links, and take advantage of the MIMO ability to flexibly select among different transmission strategies. The constrains and flexibility, however, make the MIMO deployment problem different from conventional single-antenna deployment schemes and much more challenging. Based on the constraints, we formulate the MIMO relay deployment problem, and provide a polynomial-time approximation scheme (PTAS) algorithm, as well as a distributed heuristic algorithm. The performance of the proposed algorithms is evaluated through simulations and demonstrated to be very effective.

In recent years, we have seen a surge of interest in enabling communications over meshed wireless networks. Particularly, supporting peer-to-peer communications over a multi-hop wireless network has a big potential in enabling ubiquitous computing. However, many wireless nodes have limited capabilities, for example, sensor nodes or small handheld devices. Also, the end-to-end capacity and delay degrade significantly as the path length increases with the number of network nodes. In these scenarios, the deployment of a backbone network could potentially facilitate higher performance network communications. In this paper, we study the novel Reinforcement Back-bone Network (RBN) deployment problem considering the practical limitation in the number of available backbone nodes and enforcing backbone network connectivity. We propose an iterative and adaptive (ITA) algorithm for efficient backbone network deployment. In addition, in order to provide the performance bound, we redefine and solve the problem by implementing the Generic Algorithm. Finally, we present our simulation results under various settings and compare the performance of the proposed ITA algorithm and the generic algorithm. Our study indicates that the proposed ITA algorithm is promising for deploying a connected RBN with a limited number of available backbone nodes.

Multiple-input and multiple-output (MIMO) technique is considered as one of the most promising emerging wireless technologies that can significantly improve transmission capacity and reliability in wireless mesh networks. While MIMO has been widely studied for single link transmission scenarios in physical layer as well as from MAC perspective, its impact on network layer, especially its interaction with routing has not drawn enough research attention. In this paper, we investigate the problem of routing in MIMO-based wireless mesh networks. We mathematically formulate the MIMO-enabled multi-source multi-destination multi-hop routing problem into a multi-commodity flow problem by identifying the specific opportunities and constraints brought by MIMO transmissions, in order to provide the fundamental basis for MIMO-aware routing design. We then use this formulation to develop a polynomial time approximation solution that maximizes the scaling factor for the concurrent flows in the network. Moreover, we also consider a more practical case where controllers are distributed, and propose a distributed algorithm to minimize the congestion in the network links based on steepest descent framework, which is proved to provide a fixed approximation ratio. The performance of the algorithms is evaluated through simulations and demonstrated to outperform the counterpart strategies without considering MIMO features.

With the popularity of wireless devices and the increase of computing and storage resources, there are increasing interests in supporting mobile computing techniques. Particularly, ad hoc networks can potentially connect different wireless devices to enable more powerful wireless applications and mobile computing capabilities. To meet the ever increasing communication need, it is important to improve the network throughput while guaranteeing transmission reliability. Multiple-input-multiple-output (MIMO) technology can provide significantly higher data rate in ad hoc networks where nodes are equipped with multi-antenna arrays. Although MIMO technique itself can support diversity transmission when channel condition degrades, the use of diversity transmission often compromises the multiplexing gain and is also not enough to deal with extremely weak channel. Instead, in this work, we exploit the use of cooperative relay transmission (which is often used in a single antenna environment to improve reliability) in a MIMO-based ad hoc network to cope with harsh channel condition. We design both centralized and distributed scheduling algorithms to support adaptive use of cooperative relay transmission. Our algorithm effectively exploits the cooperative multiplexing gain and cooperative diversity gain to achieve higher data rate and higher reliability under various channel conditions. Our scheduling scheme can efficiently invoke relay transmission without introducing significant signaling overhead as conventional relay schemes, and seamlessly integrate relay transmission with multiplexed MIMO transmission. We also design a MAC protocol to implement the distributed algorithm. Our performance results demonstrate that the use of cooperative relay in a MIMO framework could bring in a significant throughput improvement in all the scenarios studied, with the variation of node density, link failure ratio, packet arrival rate and retransmission threshold.

With the fast progress of multiple-input-multiple-output (MIMO) technology and its growing applications in networks, it is important to develop techniques to enable more efficient MIMO network communications. However, it is very challenging to coordinate node transmissions in a MIMO-based ad hoc network. In this work, we propose schemes that take advantage of the meshed topology of ad hoc networks to fully exploit the multiuser diversity and spatial diversity in order to maximize the data rate of the network while supporting different transmission priorities, reducing transmission delay, and ensuring fair transmissions among nodes. We formulate a concrete physical model and present cross-layer centralized and distributed scheduling algorithms that exploit physical-layer channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes. We also propose a new MAC scheme to support our distributed algorithm. The performance of our algorithms are studied through extensive simulations, and the results demonstrate that our algorithms are very effective and can significantly increase the network throughput while reducing the transmission delay.

Multiple-input and multiple-output (MIMO) technique is considered as one of the most promising wireless technologies that can significantly improve transmission capacity and reliability. Many emerging mobile wireless applications require peer-to-peer transmissions over an ad hoc network, where the nodes often have different number of antennas, and the channel condition and network topology vary over time. It is important and challenging to develop efficient schemes to distributively coordinate transmission resource sharing among a heterogeneous group of nodes over an infrastructure-free mobile ad hoc network. In this work, we propose a holistic distributed scheduling algorithm that can adaptively select different transmission strategies based on the node types and channel conditions to effectively relieve the bottleneck effect caused by nodes with smaller antenna arrays, and avoid transmission failure due to violation of channel constraint. The algorithm also takes advantage of channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes and provide special transmission support for higher priority nodes with weak channels, so that the data rate of the network can be maximized while user transmission quality requirement is supported. The performance of our algorithm is studied through extensive simulations and the results demonstrate that our algorithm is very effective in handling node heterogeneity and channel constraint, and can significantly increase the throughput while reducing the transmission delay.

It is important and challenging to develop efficient schemes to coordinate node transmissions in a MIMO-based ad hoc network. In this work, we propose a scheme to fully exploit the multiuser diversity and spatial diversity by taking advantage of the meshed topology, while also supporting user transmission quality requirement. We formulate a concrete physical model, and present cross-layer algorithms which take advantage of physical layer channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes, so that the data rate of the network can be maximized. The performance of our algorithm is studied by extensive simulations and the results demonstrate that our algorithm is very effective and can significantly increase the throughput while reducing the transmission delay.