Indoor navigation using the existing wireless infrastructure and mobile devices is a very active research area. The major challenge is to leverage the extensive smartphone sensor suite to achieve location tracking with high accuracy. In this paper, we develop a navigation algorithm which fuses the WiFi received signal strength indicator (RSSI) and smartphone inertial sensor measurements. A sequential Monte Carlo filter is developed for inertial sensor based tracking, and a radiolocation algorithm is developed to infer mobile location based on RSSI measurements. The simulation results show that the proposed algorithm significantly outperforms the extended Kalman filter (EKF), and achieves competitive location accuracy compared with the round trip time (RTT) based ultra-wideband (UWB) system.

In this paper, we develop a cooperative IMU/radio-location-based navigation system, where each node tracks the location not only based on its own measurements, but also via collaboration with neighbor nodes. The key problem is to design a nonlinear filter to fuse IMU and radiolocation information. We apply the Rao-Blackwellization method by using a particle filter and parallel Kalman filters for the estimation of orientation and other states (i.e., position, velocity, etc.), respectively. The proposed method significantly outperforms the extended Kalman filter (EKF) in the set of simulations here.

In wireless location-aware networks, mobile nodes (agents) typically obtain their positions using the range measurements to the nodes with known positions. Transmit power allocation not only affects network lifetime and throughput, but also determines localization accuracy. In this paper, we present an optimization framework for robust power allocation in network localization with imperfect knowledge of network parameters. In particular, we formulate power allocation problems to minimize localization errors for a given power budget and show that such formulations can be solved via conic programming. Moreover, we design a distributed power allocation algorithm that allows parallel computation among agents. The simulation results show that the proposed schemes significantly outperform uniform power allocation, and the robust schemes outperform their non-robust counterparts when the network parameters are subject to uncertainty .

In wireless localization systems, mobile nodes (agents) typically obtain their positions through ranging with respect to fixed infrastructure (anchors). Transmission power allocation not only affects network lifetime, throughput and interference, but also determines the localization accuracy. In this paper, we develop a robust anchor power allocation strategy to combat imperfect network topology parameters. We formulate the problem to minimize the squared position error bound (SPEB), which characterizes the fundamental limit of localization accuracy, and show that such formulation can be efficiently solved via semidefinite programming (SDP). The simulation results show that the proposed robust scheme significantly outperforms both non-robust scheme and uniform power allocation.

This paper investigates the number of multipath components in realistic wireless indoor channels. A measurement campaign was performed to collect channel realizations in the Stata Center on the MIT campus. We develop an algorithm with low computational complexity to extract multipath components from the measured waveforms. Based on the results obtained from the measured data, we quantify the behavior of the number of paths with respect to both center frequency and bandwidth.

Many future wireless applications rely on the availability of position information for mobile wireless nodes (agents). Such information can be obtained through ranging and communication between agents and fixed infrastructure (anchors). Since the transmission power of the anchors affects network lifetime, throughput, and interference, in this paper we will investigate the problem of power allocation among anchors. We start with a formulation of minimizing the squared position error bound (SPEB), which characterizes the limits of location accuracy. However, the problem is difficult to solve due to its non-convexity. To address this issue, we leverage the insights obtained from the geometric interpretation of localization information and formulate the objective to maximum directional position error bound (DPEB). We first solve the problem for the single-agent case, and then extend the algorithm for the multiple-agent case. The simulation results show that the proposed scheme achieves close-to-optimal solution but with much lower computational complexity.

Fueled by the promises of high spectral efficiency, adaptive OFDMA has attracted enormous research interests over the last decade. The significant capacity gain of adaptive OFDMA comes from fast adaptation of resource allocation in response to instantaneous channel conditions. Despite years of efforts to improve the practicality of adaptive OFDMA, such promising technique is still far from real implementation due to the prohibitively high computational complexity and excessive control overhead. This paper is an endeavor to address the problem by proposing a slow adaptation scheme, where resource allocation is adapted on a much slower time scale than the fluctuation of wireless channel fading. Specifically, the slow adaptive OFDMA is formulated into a stochastic programming problem, which adapts resource allocation according to the channel statistics within an adaptation window rather than according to instantaneous channel conditions. By tuning the length of the adaptation window, we could engineer a desirable tradeoff between spectral efficiency and computational complexity. Furthermore, the proposed scheme can be modified to accommodate inelastic traffics. The modification, referred to as "safe" slow adaptation, ensures worst-case data rates to all users. In this work, safe slow adaptation is formulated into a conic linear program, which is efficiently solved via interior-point methods. Through extensive simulations, we show that the proposed schemes drastically reduce the computational complexity and control overheads, while achieving satisfactorily high spectral efficiency and QoS provisioning as their fast-adaptation counterpart does with a much higher cost.