Frequency optimization has a great impact on lots of network Key Performance Indicators (KPIs). Conventional approaches focus on direct mitigating contention or interference among Access Points (APs), ignoring the network KPIs and their conflicts. In this paper, we propose a novel Frequency Self-Optimization (FSO) scheme to improve network KPIs in dense Wireless Local Area Networks (WLANs) where network environment is highly dynamic. By modeling the network load and related KPIs, we can derive an optimal frequency assignment solution for all APs. We formulate this problem as a multi-objective optimization problem and propose an Evolutionary Particle Swarm Optimization (EPSO) algorithm to reduce the user dissatisfaction degree and service interruption ratio, and improve the network throughput as well. Simulation results show that the proposed scheme greatly improves network KPIs and outperforms the traditional ones e.g. Dynamic access point Load-based Plan (DYLD) scheme.

Newly popular indoor location-based services (ILBS), when integrated with commerce and public safety, offer a promising land for wireless indoor localization technologies. WLAN is suggested to be one of the most potential candidates owing to its prevalent infrastructure (i.e., access points (APs)) and low cost. However, the overall performance can be greatly degraded by the spatial localizability variance problem, i.e., the localization accuracy across various locations may have significant differences given any fixed AP deployment. As a result, it brings in user experience inconsistency which is unfavorable for ILBS. In this paper, we propose NomLoc - an indoor localization system using nomadic APs to address the performance variance problem. The key insight of NomLoc is to leverage the mobility of nomadic APs to dynamically adjust the WLAN network topology. A space partition (SP)-based localization algorithm is tailored for NomLoc to perform calibration-free positioning. Moreover, fine-grained channel state information (CSI) is employed to mitigate the performance degradation of the SP-based method due to multipath and none-line-of-sight (NLOS) effects. We have implemented the NomLoc system with off-the-shelf devices and evaluated the performance in two typical indoor environments. The results show that NomLoc can greatly mitigate spatial localizability variance and improve localization accuracy with the assistance of nomadic APs as compared with the corresponding static AP deployment. Moreover, it is robust to the position error of nomadic APs.

Many emerging applications such as intruder detection and border protection drive the fast increasing development of device-free passive (DfP) localization techniques. In this paper, we present Pilot, a Channel State Information (CSI)-based DfP indoor localization system in WLAN. Pilot design is motivated by the observations that PHY layer CSI is capable of capturing the environment variance due to frequency diversity of wideband channel, such that the position where the entity located can be uniquely identified by monitoring the CSI feature pattern shift. Therefore, a ``passive'' radio map is constructed as prerequisite which include fingerprints for entity located in some crucial reference positions, as well as clear environment. Unlike device-based approaches that directly percepts the current state of entities, the first challenge for DfP localization is to detect their appearance in the area of interest. To this end, we design an essential anomaly detection block as the localization trigger relying on the CSI feature shift when entity emerges. Afterwards, a probabilistic algorithm is proposed to match the abnormal CSI to the fingerprint database to estimate the positions of potential existing entities. Finally, a data fusion block is developed to address the multiple entities localization challenge. We have implemented Pilot system with commercial IEEE 802.11n NICs and evaluated the performance in two typical indoor scenarios. It is shown that our Pilot system can greatly outperform the corresponding best RSS-based scheme in terms of anomaly detection and localization accuracy.

In this paper, we address the capacity and coverage optimization (CCO) problem for LTE networks by means of self-organizing network (SON) techniques. A novel hybrid two-layer optimization framework is proposed to enhance the network capacity and coverage, where on the top layer a network entity of eCoordinator is implemented to ensure overall network coverage by optimizing the antenna tilt and capacity-coverage weight of each cell in a centralized manner, and on the bottom layer individual eNB optimizes cell-specific capacity and coverage by tuning its pilot power in a distributed manner. A heuristic algorithm is developed for the eCoordinator operation at large time granularity and the Genetic Programming (GP) approach is exploited for the eNB operation at small time granularity, for the purpose of tracking overall network performance as well as adapting to network dynamics. Our simulation results have demonstrated the usefulness of the proposed algorithms by enhancing network capacity and coverage performance under various system requirements.

Effective coordination can dramatically reduce radio interference and avoid packet collisions for multistation wireless local area networks (WLANs). Coordination itself needs consume communication resource and thus competes with data transmission for the limited wireless radio resources. In traditional approaches, control frames and data packets are transmitted in an alternate manner, which brings a great deal of coordination overhead. In this paper, we propose a new communication model where the control frames can be "attachedâ to the data transmission. Thus, control messages and data traffic can be transmitted simultaneously and consequently the channel utilization can be improved significantly. We implement the idea in OFDM-based WLANs called hJam, which fully explores the physical layer features of the OFDM modulation method and allows one data packet and a number of control messages to be transmitted together. hJam is implemented on the GNU Radio testbed consisting of eight USRP2 nodes. We also conduct comprehensive simulations and the experimental results show that hJam can improve the WLANs efficiency by up to 200 percent compared with the existing 802.11 family protocols.

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in orthogonal frequency division multiplexing systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.

The available spectrum in cognitive radio networks is usually discontinuous but wide, which provides abundant frequency domain diversity. In this paper, we identify the opportunity of leveraging the newly-emerged technique interference alignment to exploit such diversity to support concurrent transmission and improve the network throughput in secondary networks. To enable interference alignment, independent-fading subcarriers should be grouped together to provide sufficient dimensions for intended signals and non-intended interferences at the receiver side. We formulate the subcarrier grouping problem for interference alignment to maximize the number of concurrent transmissions, and propose a greedy-based algorithm to solve it, which is proved to be optimal. Simulation results show that using the proposed scheme, the total throughput in cognitive radio networks can be greatly improved.

WLAN-based indoor location fingerprinting has been attractive owing to the advantages of open access and high accuracy. Most fingerprinting-based systems so far rely on the received signal strength (RSS), which can be easily measured at the receiver with commercial WLAN equipment. However, RSS is a coarse value which simply measures the received power for a whole channel. Thus, it fluctuates over time in typical indoor environments with rich multipath effects and not unique for a specific location. In this paper, we present the design, implementation, and evaluation of a Fine-grained Indoor Fingerprinting System (FIFS). FIFS explores a PHYlayer Channel State Information (CSI) that specifies the channel status over all the subcarriers for location fingerprinting in WLAN. The system leverages the CSI values including different amplitudes and phases at multiple propagation paths, known as the frequency diversity, to uniquely manifest a location. Moreover, the multiple antennas provides the spatial diversity that can be further augmented in fingerprinting. We also present a coherence bandwidth-enhanced probability algorithm with a correlation filter to map object to the fingerprints. We conducted experiments in two typical indoor scenarios with commercial IEEE 802.11 NICs. The experimental results demonstrate that the overall positioning accuracy can be improved compared with the RSS-based Horus system.

Device-free passive (Dfp) motion detection seeks to monitor the position change of entities without actively carrying any physical devices. Recently, WLAN with a rich set of installed wireless infrastructures enables motion detection in the area of interest. WLAN-enabled DfP motion detection rely on received signal strength (RSS) is verified to be able to provide acceptable high accuracy. Although RSS can be easily measured with commercial equipments, it is suspectable to measurement itself due to multipath effect in indoor environment. In this paper, we present an Indoor device-free Motion Detection system (FIMD) to overcome the preceding RSS-based limitation. FIMD explores properties of Channel State Information (CSI) from PHY layer in OFDM system. FIMD is designed based on the insight that CSI maintains temporal stability in static environment, while exhibits burst patterns when motion takes place. Motivated by this observation, FIMD uses a novel feature extracted from CSI to leverage its temporal stability and frequency diversity. The motion detection is conducted with outliers identification from normal features in continuous monitoring using density-based DBSCAN algorithm. Moreover, we leverage two schemes including false alert filter and data fusion to enhance the detection accuracy. We implement FIMD system with commercial IEEE 802.11n NICs and evaluate its performance in two typical indoor scenarios. Experiment results show that FIMD can achieve high detection rate. Moreover, comparing with RSSI, the feature extracted from CSI enables better detection performance in accuracy and robustness to narrowband interference.

Effective coordination can dramatically reduce radio interference and avoid packet collisions for multi-station wireless local area networks (WLANs). Coordination itself needs consume communication resource and thus competes with data transmission for the limited wireless radio resources. In traditional approaches, control frames and data packets are transmitted in an alternate manner, which brings a great deal of coordination overhead. In this paper we propose a new communication model where the control frames can be “attached” to the data transmission. Thus, control messages and data traffic can be transmitted simultaneously and consequently the channel utilization can be improved significantly. We implement the idea in OFDM-based WLANs called hJam, which fully explores the physical layer features of the OFDM modulation method and allows one data packet and a number of control messages to be transmitted together. hJam is implemented on the GNU Radio testbed consisting of eight USRP2 nodes. We also conduct comprehensive simulations and the experimental results show that hJam can improve the WLANs efficiency by up to 72% compared with the existing 802.11 family protocols.

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance is a big challenge. In this work, we analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.

The concept of femtocell that operates in licensed spectrum to provide home coverage has attracted interest in the wireless industry due to high spatial reuse, and extensive deployments of femtocells is expected in the future. In this paper, we consider the scenario that a femtocell service provider (FSP) expects to rent spectrum from the coexisting macrocell service provider (MSP) to serve its end users. In addition to the spectrum leasing payment, the FSP may allow hybrid access of macrocell users to improve the utilities of itself and MSP, which are defined as the sum of data traffic and payment/revenue. We propose the spectrum leasing framework taking hybrid access into consideration. The whole procedure is modeled as a three-stage Stackelberg game, where MSP and FSP determine the spectrum leasing ratio, spectrum leasing price and open access ratio sequentially to maximize their utilities, and the existence of the Nash Equilibrium of the sequential game is analyzed. We characterize the equilibrium, in terms of access price, spectrum acquisition of FSP, the open access ratio, and price of anarchy via simulation. Numerical results show that both MSP and FSP can benefit from spectrum leasing, and hybrid access of femtocell can further improve their utilities, which provide sufficient incentive for their cooperation.

ABSTRACT In this paper, we consider the narrowband interference problem for orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems, in which parts of the OFDM subcarriers and parts of the data frame can be seriously interfered, resulting in significant performance degradation. We propose a scheme of iterative noise plus interference estimation and decoding (IED) to mitigate the performance degradation caused by the narrowband interference, which is based on expectation maximization (EM) algorithm. To reduce the number of OFDM symbols for time domain averaging required in the proposed scheme, and adapt the proposed scheme to rapid changing narrowband interference conditions, we also propose an IED scheme with frequency domain partial averaging (IED‐FPA). Moreover, we derive the Cramér‐Rao lower bounds for unbiased noise plus interference variance estimations, and they can be achieved via the proposed IED schemes. Simulation results show that the proposed IED‐FPA scheme can effectively achieve the same performance as that of the optimal maximum likelihood decoder with full knowledge of the power plus interference variances, and the proposed IED‐FPA scheme is very robust with respect to the number of the interfered subcarriers and positive errors of the knowledge of the interfered subcarriers' number. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we propose a novel distributed navigation algorithm for people to escape from critical event region in wireless sensor networks (WSNs). Unlike existing works, the scenario discussed in the paper has no goal or exit as guidance, leading to a big challenge for the navigation problem. To solve it, our proposed navigation algorithm computes the convex hull of the event region just by some topological methods. With the reference of the convex hull, people can be easily navigated out of the event region. Both the computation complexity and communication overhead of the proposed algorithm are very low, as it only needs to flood two shortest path trees in a limited area around the event region with a distance [L/2π] + 1 to the event boundary, where L is the length of the boundary. Conducted simulations have verified the effectiveness and scalability of the proposed algorithm.

In this paper, we focus on the dynamic spectrum access of infrastructure-based cognitive radio networks, a primary network and multiple secondary networks, which are collocated with each other. To improve network performance of all networks, we propose a cooperative communication-aware spectrum leasing framework. In the proposed framework, the primary network leverages secondary APs as cooperative relays, and decides the optimal strategy on the relay selection and the price for spectrum leasing. Based on primary network's strategy, secondary networks determine the length of spectrum access time they purchase from the primary network. Finally, each network allocates the total spectrum access time of the network among its end users. The above sequential decision procedure is formulated as a Stackelberg game, with primary network acting as the leader and secondary networks as the followers, and a unique Nash Equilibrium (NE) point is achieved through backward induction analysis. At this NE point, all networks maximize their utilities in terms of transmission rate and revenue/payment. Simulation results show that the primary network and secondary networks achieve higher utilities by exploiting cooperative transmission under our proposed framework, which gives all networks incentive for cooperation.

In this paper, we propose an iterative data detection scheme to combat unknown narrowband interference for orthogonal frequency division multiplexing (OFDM) systems. The key idea of the proposed scheme is to joint channel estimation, noise plus interference power estimation and decoding together to enhance both the estimation accuracy and bit error ration (BER) performance. We derive the Cramér-Rao Bounds (CRB) for mean square errors of channel estimation and noise plus interference estimations, and the CRBs can be achieved via the proposed scheme with very few iteration numbers, hence, the complexity of the proposed scheme is very low. Moreover, simulation results show that the decoding performance of the proposed scheme substantially approaches to that of the maximum likelihood decoder with perfect channel estimation and full knowledge of interference distribution.