Lang Tong

Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework

We propose decentralized cognitive MAC protocols that allow secondary users to independently search for spectrum opportunities without a central coordinator or a dedicated communication channel. Recognizing hardware and energy constraints, we assume that a secondary user may not be able to perform full-spectrum sensing or may not be willing to monitor the spectrum when it has no data to transmit. We develop an analytical framework for opportunistic spectrum access based on the theory of partially observable Markov decision process (POMDP). This decision-theoretic approach integrates the design of spectrum access protocols at the MAC layer with spectrum sensing at the physical layer and traffic statistics determined by the application layer of the primary network. It also allows easy incorporation of spectrum sensing error and constraint on the probability of colliding with the primary users. Under this POMDP framework, we propose cognitive MAC protocols that optimize the performance of secondary users while limiting the interference perceived by primary users. A suboptimal strategy with reduced complexity yet comparable performance is developed. Without additional control message exchange between the secondary transmitter and receiver, the proposed decentralized protocols ensure synchronous hopping in the spectrum between the transmitter and the receiver in the presence of collisions and spectrum sensing errors

Blind identification and equalization based on second-order statistics: a time domain approach

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel. >

A least-squares approach to blind channel identification

Conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. In this paper, we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel FIR systems without requiring the knowledge of the input statistical model. A new blind identification algorithm based solely on the system outputs is proposed. Necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented.

A new approach to blind identification and equalization of multipath channels

A novel blind channel identification and equalization method is proposed by exploiting the cyclostationarity of communication signals. Identification and equalization of possibly nonminimum phase multipath channels are achieved without using training signals. Unlike most of the adaptive blind equalization methods for which the convergence properties are often problematic, the eigenstructure-based channel estimation algorithm proposed here is asymptotically exact. Based on the second-order properties, the proposed approach identifies nonminimum phase channels and achieves equalization with fewer samples than most techniques based on higher-order statistics. Simulations demonstrate the promising performance of the proposed algorithm in a blind equalization of a three-ray multipath channel.<<ETX>>

COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - Dynamic Spectrum Access in the Time Domain: Modeling and Exploiting White Space

Dynamic spectrum access is a promising approach to alleviate the spectrum scarcity that wireless communications face today. In short, it aims at reusing sparsely occupied frequency bands while causing no (or insignificant) interference to the actual licensees. This article focuses on applying this concept in the time domain by exploiting idle periods between bursty transmissions of multi-access communication channels and addresses WLAN as an example of practical importance. A statistical model based on empirical data is presented, and it is shown how to use this model for deriving access strategies. The coexistence of Bluetooth and WLAN is considered as a concrete example

Pilot-assisted wireless transmissions: general model, design criteria, and signal processing

Pilot-assisted transmission (PAT) multiplexes the known symbols with the information bearing data. These pilot symbols and the specific multiplexing scheme are known at the receiver and can be exploited for channel estimation, receiver adaptation, and optimal decoding. Even though PAT has been used for many practical reasons, there still remains a need for an optimal design. The theory and methodology for the design of an optimal PAT have emerged, but much still remains unknown. In this article, the author has presented an overview of PAT. A general PAT model is given as well as the review of the common design criteria. The information theoretic and signal processing issues were also discussed.

Opportunistic Spectrum Access via Periodic Channel Sensing

The problem of opportunistic access of parallel channels occupied by primary users is considered. Under a continuous-time Markov chain modeling of the channel occupancy by the primary users, a slotted transmission protocol for secondary users using a periodic sensing strategy with optimal dynamic access is proposed. To maximize channel utilization while limiting interference to primary users, a framework of constrained Markov decision processes is presented, and the optimal access policy is derived via a linear program. Simulations are used for performance evaluation. It is demonstrated that periodic sensing yields negligible loss of throughput when the constraint on interference is tight.

Decentralized cognitive mac for dynamic spectrum access

We consider the problem of opportunistic dynamic spectrum access (DSA) in an ad hoc network in which unlicensed secondary users communicate through channels not used by the primary users. Decentralized cognitive medium access control protocols are presented that allow secondary users to recognize spectrum opportunity and transmit based on a partial observation of the instantaneous spectrum availability. Under a framework of partially observable Markov decision process (POMDP), we derive optimal and suboptimal decentralized strategies for the secondary users to decide which channel(s) to sense and access for the maximization of the overall network throughput

Sensor networks with mobile agents

Architecture for large scale low power sensor network is proposed. Referred to as sensor networks with mobile agents (SENMA), SENMA exploit node redundancies by introducing mobile agents that communicate opportunistically with a large field of sensors. The addition of mobile agents shifts computationally intensive tasks away from primitive sensors to more powerful mobile agents, which enables energy efficient operations under severely limited power constraints. An opportunistic ALOHA random access coupled with a direct sequence spread spectrum physical layer is proposed. A comparison of SENMA with a flat ad hoc sensor network shows a substantial gain in energy efficiency.

Malicious Data Attacks on the Smart Grid

Malicious attacks against power systems are investigated, in which an adversary controls a set of meters and is able to alter the measurements from those meters. Two regimes of attacks are considered. The strong attack regime is where the adversary attacks a sufficient number of meters so that the network state becomes unobservable by the control center. For attacks in this regime, the smallest set of attacked meters capable of causing network unobservability is characterized using a graph theoretic approach. By casting the problem as one of minimizing a supermodular graph functional, the problem of identifying the smallest set of vulnerable meters is shown to have polynomial complexity. For the weak attack regime where the adversary controls only a small number of meters, the problem is examined from a decision theoretic perspective for both the control center and the adversary. For the control center, a generalized likelihood ratio detector is proposed that incorporates historical data. For the adversary, the trade-off between maximizing estimation error at the control center and minimizing detection probability of the launched attack is examined. An optimal attack based on minimum energy leakage is proposed.