OFDMA femtocell is a promising technology to improve indoor cellular network coverage cost-effectively. Large-scale deployment of femtocells in the urban area is expected to be realized in the near future. However, inter-femtocell interference significantly limits the achievable throughput of an OFDMA femtocell system, which calls for interference management tailored for femtocell networks. A typical approach to mitigate inter-femtocell interference is known as resource isolation, which aims at assigning non-overlapping resources to interfering femtocells. One of the main challenges for interference mitigation in femtocell networks is that end consumers often install the femtocells. Very limited information about the femtocells is available, making it hard to decipher the inter-femtocell interference. Previous studies either take time to resolve collisions online or adopt a conservative approach to identify interferers. Although the latter approach avoids wasting time on resolving collisions, it may result in resource underutilization. In this paper, we propose an efficient method to identify inter-femtocell interference by analyzing the received patterns observed by mobile stations. We conducted experiments on GNU Radio/USRP to demonstrate that the proposed interference identification method can successfully identify real interferers while excluding non-interfering femtocells from suspect femtocells. Based on the proposed interference identification, we propose a weighted vertex-coloring based resource assignment algorithm to allocate resources with better fairness and higher throughput.

mmWave bands have large chunks of spectrum and are promising for satisfying the unprecedented cellular traffic demands of future cellular networks. Unfortunately, the characteristic that mmWaves have high attenuation against physical objects and even atmosphere hinder its feasibility. High gain directional antennas are required to combat the attenuation. However, user mobility might change the signal direction, introduce physical objects in the line-of-sight of the signal and might block the directional signal. Additionally, user mobility might also make two directional signals to interfere with one another. The blockage and interference incidents must be handled with care. In this paper, the mmWave-enabled HetNet cells record the fingerprints of the incidents and the cells' transmission parameters. When a user is moving, and the serving cell's transmission parameters are approaching a fingerprint, the HetNet takes necessary actions to mitigate interference or avoid disconnection before it happens. Our results show that for typical indoor scenarios, most of the blockage and interference can be predicted so that countermeasures can be applied in advance.

In cognitive radio networks (CRNs), the established communication sessions between secondary users (SUs) may be affected or even get interrupted because the SUs need to relinquish the spectrum when the licensed users (PUs) appear and reclaim the spectrum/channel. On detecting PU activities, the SUs on the affected links either switch to another available idle spectrum using the same link or the SUs seek for an alternative path/link. In either approach, the ongoing session is destined to experience delay or even gets interrupted, which is intolerable to quality of service-sensitive applications such as multimedia streaming or audio/video conferencing. In this paper, we study the problem of establishing k-protected routes in CRNs. A k-protected route consists of a set of main links with preassigned backup spectrum and backup paths and is guaranteed to sustain from k PU appearances without being interrupted. For a CRN, we find a k-protected route for each session request and maximize the number of sessions that can be supported. We propose both centralized and distributed k-protected routing algorithms for this problem. Simulation results show that our k-protected routing protocol outperforms existing opportunistic spectrum switching approaches in terms of delay and interruption rate.

mm-Wave is a promising technology to meet the enormous bandwidth demands of the future generation cellular networks. This technology has vast amount of unused bandwidth, but has problem of human blockage. Blockage mitigation methods for indoor environments cannot be applied to outdoor scenarios effectively. In this paper, we mitigate human blockage of the mm-Wave technology by proposing an algorithm that provides intelligent user association in mm-Wave networks. The proposed algorithm collects the history blockage incidents throughout the network and exploits the history incidents to associate user equipment to the base station with lower blockage possibility. The blockage incidents happened at different locations in the network. When user equipment attempts to find a base station to associate to, the algorithm examines the history blockage incidents near the location of the user equipment. In this way, the user equipment is associated to a base station that has smaller chance of being blocked. The simulation results show that our proposed algorithm is performing better in terms of improving SINR, rate of the links and blockage rate in the network compared to another state-of-the-art user association algorithm designed for mm-Wave networks and common user association algorithms of associating user to closest base station and base station with maximum SINR.

Network densification by installing more cellular stations (cells) with smaller coverage is a promising technique to improve wireless capacity to meet the overwhelming demands for mobile data usage. These smaller cells with different coverage and the macrocellular base station form heterogeneous cellular networks (HetNet). However, the dense deployment of HetNet cells might result in unexpected inter-cell interference. In cellular networks, the data to all cells and to the mobile stations (MSs) are originated from the core cellular network. We take advantage of this characteristic and propose a technique called interference precancellation. If the interferer to an MS is identified, the victim cell that serves the victim MS transmits the interference precanceled signal, which is the signal intended for the victim MS subtracts the interference signal. The interference precanceled signal and the interference signal scramble at the victim MS and become the intended signal. With interference precancellation, the interferer and the victim cell can utilize the same wireless resources and thus the capacity is further improved. However, some MSs are interference-free and for MSs whose exact interferers cannot be determined, the MSs still require isolated resources. We propose an algorithm for resource management with interference precancellation (RMIP) that jointly considers MSs experiencing different level of interference. Through experiments on GNURadio/USRP, we show that the known interference signal can be precanceled and the combination of the interference signal and the precanceled signal becomes the intended signal. Through simulation, we evaluate the performance in larger HetNets.

In cognitive radio (CR) networks, control channels are needed for secondary users (SUs) to perform certain handshaking procedures to negotiate communication parameters and to exchange control messages. The process of SUs attempting to meet on a channel is known as rendezvous. Previous studies either use a dedicated control channel or adopt channel-hopping schemes to rendezvous. However, it is not realistic to designate a preselected control channel in licensed spectrum bands and the time consumption for channel hopping grows drastically. Moreover, a requirement of previous studies is that a common channel must be available across the whole network at all SUs. Such a channel might not exist and the SUs cannot form an effective CR network. This paper proposes an efficient approach for guaranteed NC-OFDM-based Control Channel (NCCC) establishment by utilizing subcarrier pulses. Simulation results show that the time needed for establishing control channel is lower than that of channel hopping schemes. Additionally, the proposed approach is able to establish NCCC even if there is no common channel in the CR network. NC-OFDM-based control interfaces improve the control channel establishment rate even when the interface can only access the spectrum bandwidth that is equal to one channel.

Duty cycling improves energy efficiency but limits throughput and introduces significant end-to-end delay in wireless sensor networks. In this article, we present a traffic-adaptive synchronous MAC protocol (TAS-MAC), which is a high-throughput, low-delay MAC protocol tailored for low power consumption. It achieves high throughput by adapting time division multiple access (TDMA) to a novel traffic-adaptive allocation mechanism that assigns time slots only to nodes located on active routes. TAS-MAC reduces the end-to-end delay by notifying all nodes on active routes of incoming traffic in advance. These nodes will claim time slots for data transmission and forward a packet through multiple hops in a cycle. The desirable traffic-adaptive feature is achieved by decomposing traffic notification and data-transmission scheduling into two phases, specializing their duties and improving their efficiency, respectively. Simulation results and experiments on TelosB motes demonstrate that the two-phase design significantly improves the throughput of current synchronous MAC protocols and achieves the similar low delay of slot-stealing-assisted TDMA with much lower power consumption.

To improve spectrum utilization, cognitive radio (CR) is introduced to detect and exploit available spectrum resources autonomously. The flexible spectrum use imposes special challenges on broadcast because different CR devices may detect different available spectrum fragments at different locations. The sender and the receivers have to agree on spectrum fragments that will be used for broadcast. There may not exist a common spectrum fragment that is available to all receivers. Most existing work assumes that a device works only in a single channel and thus the sender has to broadcast multiple times in different channels to reach all receivers. The broadcast problem is studied as a channel rendezvous and minimum latency scheduling problem. Recent spectrum-agile designs have enabled a device to utilize partially occupied spectrum. We thus view a wideband channel as an aggregation of multiple narrow channels that can be evaluated independently. A Spectrum Fragment Agile Broadcast (SFAB) scheme is introduced in this paper to support efficient broadcast on fragmented spectrum. It aims at achieving spectrum agreement between the transmitter and the receivers efficiently and maximizing the channel width used for broadcast regardless of the spectrum availability differences at receivers. We validate the effectiveness of SFAB through implementation on the GNU Radio / USRP platform and use ns-2 simulations to evaluate the performance in large deployments.

Inter-femtocell interference significantly limits the achievable throughput of an OFDMA femtocell system, which calls for interference management tailored for femtocell net-works. A typical approach to mitigate inter-femtocell interference is known as resource isolation, which aims at assigning nonoverlapping resources to interfering femtocells. One of the main challenges for interference mitigation in femtocell networks is that the femtocells are often installed by end-consumers without any pre-planning. Very limited information about the femtocells is available, making it hard to decipher the inter-femtocell interference. In this paper, we propose an efficient method to identify inter-femtocell interference by analyzing the received patterns observed by mobile stations. We conducted experiments to demonstrate that the proposed interference identification method can successfully identify real interferers while excluding non-interfering femtocells from suspicious interferers. Based on the proposed interference identification, we propose a weighted vertex-coloring based resource assignment algorithm to allocate resources with better fairness and achieve higher throughput.

Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. When unlicensed users opportunistically utilize spectrum holes, prediction models that infer the availability of spectrum holes can help to improve the spectrum extraction rate and reduce the collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims at identifying frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users are able to utilize spectrum holes aggressively without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real-world Wi-Fi and personal communication service (PCS) activities, we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved. Further, we extend the three-state PPPM to an N-state PPPM to predict the duration of high/low utilization in a channel. The frequent patterns of channel utilization duration are critical in optimizing channel switch strategies. The high prediction accuracy is validated with data collected in the paging bands.

Duty cycling improves energy efficiency but limits throughput and introduces significant end-to-end delay in wireless sensor networks. In this paper, we present a traffic-adaptive synchronous MAC protocol (TAS-MAC), which is a high throughput low delay MAC protocol tailored for low power consumption. It achieves high throughput by using Time Division Multiple Access (TDMA) with a novel traffic-adaptive allocation mechanism that assigns time slots only to nodes located on active routes. TAS-MAC reduces the end-to-end delay by notifying all nodes on active routes of incoming traffic in advance. These nodes will claim time slots for data transmission and forward a packet through multiple hops in a cycle. The desirable traffic-adaptive feature is achieved by decomposing traffic notification and data transmission scheduling into two phases, specializing their duties and improving their efficiency respectively. Simulation results and tests on TelosB motes demonstrate that the two-phase design significantly improves the throughput of current synchronous MAC protocols and achieves the similar low delay of slot stealing assisted TDMA with much lower power consumption.

Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to reduce collision and improve spectrum extraction. This paper introduces a Partial Periodic Pattern Mining (PPPM) algorithm to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly. Using real life network activities, we show a significant reduction on miss rate in channel state prediction.

Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. The availability of spectrum holes vastly affects the throughput and delay of unlicensed users. Predictive methods for inferring the availability of spectrum holes can help to improve spectrum extraction rate and reduce collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims to identify frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users will be able to make use of spectrum holes efficiently without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real life network activities we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved.

Most debris flow monitoring systems deployed today use indirect means to track information regarding debris flows. In this work, we introduce a novel debris flow monitoring system for in-situ and direct tracking of debris flows in real-time. The core idea is to throw wireless sensors into the debris flows and collect flow information as they move along. We will describe the design of the wireless sensors and present some preliminary performance results.

The scale of weather monitoring is limited by the cost of the automatic weather stations (AWS), which is mainly the cost of high precision instruments and long-distance wireless telecommunication equipments. We propose a wireless sensor network (WSN) based AWS, which takes advantage of the low-cost, real-time and infrastructure-free characteristics of WSN [1]. We can therefore extend the scale of weather monitoring without increasing the number of telecommunication equipments. This WSN-based AWS is able to cover a plane and gather multiple sets of weather measurements in real-time at a better data resolution.