Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we present CellSense, a probabilistic RSSI-based fingerprinting location determination system for GSM phones.We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses the challenges. To evaluate our proposed system, we implemented CellSense on Android-based

In this paper we consider a single cognitive radio seeking a transmission opportunity by sequentially sensing a number of statistically independent primary channels. We study the joint optimization of the time spent to sense a channel, the decision threshold to determine whether the channel is free or busy, together with the order with which the channels are sensed. The sensing time and decision threshold are assumed to be the same for all channels. The design objective is to maximize the expected secondary throughput taking sensing errors into account and penalizing for collisions that may

Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is

In this paper, we consider the problem of binary hypothesis testing for distributed detection in wireless sensor networks in which a transmission censoring scheme is employed. The sensor nodes transmit binary decisions to the fusion center (FC) for final decision making. Sensor nodes with unreliable observation samples censor transmission to FC. By having two thresholds at each sensor node, a sensor node censors transmission if its log-likelihood ratio (LLR) falls between the two thresholds, whereas the more informative sensor nodes transmit their decisions to the FC. In this case of censoring

In this paper, we examine a cognitive spectrum access scheme in which secondary users exploit the primary feedback information. We consider an overlay secondary network employing a random access scheme in which secondary users access the channel by certain access probabilities that are function of the spectrum sensing metric. In setting our problem, we assume that secondary users can eavesdrop on the primary link's feedback. We study the cognitive radio network from a queuing theory point of view. Access probabilities are determined by solving a secondary throughput maximization problem

In this paper we study the problem of buffer-aware power control in underlay cognitive radio networks. In particular, we investigate the role of buffer state information, manifested through the secondary users' queue lengths, along with channel state information in the cognitive radio power control problem. Towards this objective, we formulate a constrained optimization problem to find the set of secondary user transmit powers that maximizes the sum of rates weighted by the respective buffer lengths subject to signal-to- interference-and-noise-ratio (SINR) and maximum power constraints

A fundamental problem in cognitive radio systems is that the secondary user is ignorant of the primary channel state and the interference it inflicts on the primary license holder. We consider a secondary user that can eavesdrop on the ACK/NACK Automatic Repeat reQuest (ARQ) fed back from the primary receiver to the primary transmitter. Assuming the primary channel states follow a Markov chain, this feedback gives the secondary user an indication of the primary channel quality. Based on the ACK/NACK received, we devise optimal transmission strategies for the secondary user so as to maximize a

In this paper, we propose fractional sequential sensing (FSS) as a novel cooperative sensing scheme for cognitive radio networks. FSS compromises a tradeoff between sensing accuracy and efficiency by formulating an optimization problem whose solution identifies FSS sensing parameters. These parameters include the sensing period and channels allocated for each user. Our simulation results show that FSS successfully improves the sensing accuracy while maintaining a low power profile. Additionally, we show that the sensing accuracy performance gap between FSS and other traditional schemes

This paper proposes a new cooperative protocol which involves cooperation between primary and secondary users. We consider a cognitive setting with one primary user (PU) and multIPle secondary users (SUs). The time resource is partitioned into discrete time slots. Each time slot, one of the SUs is scheduled for transmission according to time division multIPle access scheme, and the remainder of the SUs, which we refer to as secondary relays, attempt to decode the primary packet. If more than one relay can decode the primary packet, the secondary relays then employ cooperative beamforming to

In this paper, a multiple-access wireless network consisting of two transmitters and one receiver is considered. The transmitters can access the same channel simultaneously and the receiver performs successive interference cancellation (SIC) to decode the messages from both senders. A two-dimensional Markov chain is used to model the medium access control layer behavior of the system, where the state represents the queue length of the transmitters. In this model a general number of packets can be transmitted from any user in a single time slot. A probabilistic cross-layer scheme is proposed to