In this paper, we conduct ray-tracing simulation studies using WinProp tool to understand the path loss, coverage area, diversity and delay spread of the 29GHz band in the indoor propagation environment, and compare the 29GHz indoor channel to the 2.9GHz indoor channel. In addition, we compare our simulation results with the measurement data from [4]. Based on our simulation results, we conclude that the 29GHz band has similar indoor channel characteristics as 2.9GHz and that any inherent impairment of the mmWave due to higher carrier frequency can easily compensated with beam forming techniques and other advanced algorithms. Specifically, our results show that the path loss exponents and the RMS delay spread values are similar at 29GHz and at 2.9GHz. In addition, our simulation results show that by having multiple access points on an office floor, we can significantly improve important diversity metrics such as the number of detectable beams and the angular separation of those beams, so as to make 29GHz system using beam forming technique more robust against beam blockage.

Utilization of millimeter wave (MMW) bands for supporting very high data rate cellular access has received much attention in recent times. The behavior of the wireless channel in these bands differ significantly from the sub-6 GHz counterparts. Ray tracing is a complementary effort to detailed measurements in providing quick insight into the coarse channel characteristics. In this paper, we model the 28 GHz outdoor channel through ray tracing using the WinProp tool. We simulate the single base station and multi base station scenarios in the downtown Manhattan area and study channel properties such as coverage and path loss, path diversity and delay spreads. We observe that a typical MMW base station provides coverage to a significant fraction of a 100m cell radius with path diversity, i.e. there exist secondary paths that may sustain the link on the failure of the first path. The improvements to coverage and path diversity with multiple base stations are also characterized. The above channel properties are compared with NYU's [4] results from the same region. We observe a reasonable match for many of the above parameters. The delay spreads with omni and beamformed antennas are also compared. The typical RMS delay spread in a beamformed scenario is seen to be in the order of 20-30 ns which is significantly smaller than the measured values of 100ns or more. This phenomenon is likely due to detailed clutter in the environment not being modeled.

We study an intermittently connected network (ICN) composed of multiple clusters of wireless nodes. Within each cluster, nodes can communicate directly using the wireless links. However, these clusters are far away from each other such that direct communication between the clusters is impossible except through “mobile” contact nodes. These mobile contact nodes are data carriers that shuffle between clusters and transport data from the source to the destination clusters. There are several applications of our network model, such as clusters of mobile soldiers connected via unmanned aerial vehicles. Our work here focuses on a queue-based cross-layer technique known as the back-pressure algorithm. The algorithm is known to be throughput-optimal, as well as resilient to disruptions in the network, making it an ideal candidate communication protocol for our intermittently connected network. In this paper, we design a back-pressure routing/rate control algorithm for ICNs. Though it is throughput-optimal, the back-pressure algorithm has several drawbacks when used in ICNs, including long end-to-end delays, large number of potential queues needed, and loss in throughput due to intermittency. We present a modified back-pressure algorithm that addresses these issues. We implement our algorithm on a 16-node experimental testbed and present our experimental results in this paper.

In this paper, we consider a network of stationary nodes that rely on mobile nodes to transport data between them. We assume the mobile nodes can control their mobility pattern to respond to traffic loads, as well as satisfy some other secondary objectives, such as surveillance requirements. We study this problem in the framework of cost minimization, where we derive a dual iterative algorithm that results in optimal mobility pattern for minimizing network wide cost. We then implement our proposed algorithm and evaluate its performance on a testbed.

We study a mobile wireless network where groups or clusters of nodes are intermittently connected via mobile "carriers'' (the carriers provide connectivity over time among different clusters of nodes). Over such networks (an instantiation of a delay tolerant network), it is well-known that traditional routing algorithms perform very poorly. In this paper, we propose a two-level Back- Pressure with Source-Routing algorithm (BP+SR) for such networks. The proposed BP+SR algorithm separates routing and scheduling within clusters (fast time-scale) from the communications that occur across clusters (slow time-scale), without loss in network throughput (i.e., BP+SR is throughput-optimal). More importantly, for a source and destination node that lie in different clusters, the traditional back-pressure algorithm results in large queue lengths at each node along its path. This is because the queue dynamics are driven by the slowest time-scale (i.e., that of the carrier nodes) along the path between the source and destination, which results in very large end-to-end delays. On the other-hand, we show that the two-level BP+SR algorithm maintains large queues only at a very few nodes, and thus results in order-wise smaller end-to-end delays. We provide analytical as well as simulation results to confirm our claims.

We study a network composed of multiple clusters of wireless nodes. Within each cluster, nodes can communicate directly using the wireless links; however, these clusters are far away such that direct communication between the clusters is impossible except through "mobile" contact nodes. These mobile contact nodes are data carriers that shuffle between clusters and transport data from source to destination clusters. There are several applications of our network model (e.g., clusters of mobile soldiers connected via unmanned aerial vehicles).