Satish Vedantam

Sparse Channel Estimation with Zero Tap Detection

Algorithms for the estimation of a channel whose impulse response is characterized by a large number of zero tap coefficients are developed and compared. Estimation is conducted in a two-stage fashion where an estimate of the non-zero taps is followed by channel estimation. Tap detection is transformed into an equivalent on-off keying detection problem. Several tap detection algorithms are investigated which tradeoff between complexity and performance. The proposed methods are compared to an unstructured least squares channel estimate as well as a structured approach based on matching pursuit. Three schemes in particular are developed: a sphere decoder based scheme, a Viterbi algorithm based method and a simpler iterative approach. The latter offers a better tradeoff between estimation accuracy and computational cost. A joint estimation and zero tap detection scheme is also considered. All solutions exhibit a significant gain in terms of mean-squared error and bit error rate over conventional schemes which do not exploit the sparse nature of the channel, as well as the matching pursuit approach which does endeavor to exploit the sparsity

Sparse channel estimation with zero tap detection

Algorithms for the estimation of a channel whose impulse response is characterized by a large number of negligible tap coefficients are developed and compared. Exploiting this sparsity, the estimation problem is transformed into an equivalent on-off keying detection problem, whose solution gives an indication on the position of the zero taps. The proposed schemes are compared to the standard least squares estimate via simulations in terms of mean square error and bit error rate. A scheme based on sphere decoding appears to give the best performance while maintaining moderate complexity.

Joint Transmission and State Estimation: A Constrained Channel Coding Approach

A scenario involving a source, a channel, and a destination, where the destination is interested in both reliably reconstructing the message transmitted by the source and estimating with a fidelity criterion the state of the channel, is considered. The source knows the channel statistics but is oblivious to the actual channel state realization. Herein, it is established that a distortion constraint for channel state estimation can be reduced to an additional cost constraint on the source input distribution, in the limit of large coding block length. A newly defined capacity-distortion function thus characterizes the fundamental tradeoff between transmission rate and state estimation distortion. It is also shown that noncoherent communication coupled with channel state estimation conditioned on treating the decoded message as training symbols achieves the capacity-distortion function. Among the various examples considered, the capacity-distortion function for a memoryless Rayleigh fading channel is characterized to within 1.443 bits at high signal-to-noise ratio. The constrained channel coding approach is also extended to multiple access channels, leading to a coupled cost constraint on the input distributions for the transmitting sources.

A constrained channel coding approach to joint communication and channel estimation

A joint communication and channel state estimation problem is investigated, in which reliable information transmission over a noisy channel, and high-fidelity estimation of the channel state, are simultaneously sought. The tradeoff between the achievable information rate and the estimation distortion is quantified by formulating the problem as a constrained channel coding problem, and the resulting capacity-distortion function characterizes the fundamental limit of the joint communication and channel estimation problem. The analytical results are illustrated through case studies, and further issues such as multiple cost constraints, channel uncertainty, and capacity per unit distortion are also briefly discussed.

Sensing the channel: sensor networks with shared sensing and communications

A new class of abstract sensor networks is introduced and analyzed. The object of the sensing is the inter-node channel. Examples of systems which seek to sense the channel include: underwater sonar, radar and optics based atmospheric sensor networks. In these networks, the sensing and communication tasks share the bandwidth resource in addition to the sharing of power resources as has been conventionally studied in the sensor network framework. Bounds on distortion tradeoffs are developed for various protocols, such as source coding analogues of decode-and-forward and amplify-and-forward and simple topologies such as two-hop networks (three node system)

Shared Sensing and Communications in Sensor Networks : The Multihop Case

A joint sensing/communication problem is considered for sensor networks. Herein, the channel(s) between a source and destination are the parameters to be sensed and communicated over the network. Lower bounds on the end-to-end distortion are developed for a multihop, linear network. Internode communication is assumed to be done via an encode-and-forward approach. For a many-to-one topology with two hops, data aggregation and time-division communication approaches are compared. Asymptotic in the SNR, it is shown that a time-division approach is superior

Asymptotic distortion exponents for the estimation of time-varying channels in multihop sensor networks

The problem of time-varying channel estimation in multihop sensor networks is examined. Two relay processing methods are explored: amplify-and-forward and encode-and-forward. Bounds on the end-to-end distortion for all internode channel estimates are computed for these two relay processing schemes. Performance is analyzed via the asymptotic limit of the decay rate of the end-to-end distortion with respect to SNR at high SNR. It is also established that asymptotically in SNR, amplify-and-forward can outperform encode-and-forward and in fact can achieve the maximum possible distortion exponent (distortion decay rate) order of unity. Linear and many-to-one topologies are then examined and it is shown that orthogonal access in the many-to-one network is optimal.

Minimizing sum distortion for static and mobile fusion center placement in underwater sensor networks

The problem of optimizing the position of a mobile fusion center in a sensor network is considered. The optimization criterion of interest is the sum distortion for the communication of all the information from each of the nodes to the fusion center. Transmission losses along underwater links are modeled and a time-multiplexing architecture imposed on the sensor nodes for communication with the fusion center. Classical results from the information theory literature are leveraged and the optimization problem is formulated and solved analytically for the case when the fusion center is at one fixed location and numerically for the case when the fusion center is mobile. It is observed that in the low node power regime, with the fusion center at a fixed location, the fusion center selectively communicates with a few nodes while turning the others off. In the high power regime, the time allocated to the nodes is a function of the information they need to transmit to the destination and the distance from the node to the fusion center. Further, when the fusion center is mobile, in the low power regime, the fusion center is placed close to the node with the largest information content while for higher powers the difference from a fixed fusion center declines.

Joint communication and channel estimation: the two hop case

The problem of communicating over a two hop network while estimating the channel states to within a distortion constraint is explored. The end to end capacity of this network is upper and lower bounded. Under certain conditions, it is shown that the upper and lower bounds match and we have the capacity of the network. An example is also presented to apply the results developed.

Joint Channel Estimation and Data Transmission: Achievable Rates

A formulation for a joint communication and estimation problem is proposed: simultaneous communication over a noisy channel and estimation of certain channel parameters are desired. We are interested in quantifying the tradeoff between the achievable rate and distortion in estimating the channel parameters. Two particular sample channels are considered and achievable rates for these channels are determined. First, the binary symmetric channel is examined; an achievable capacity-distortion tradeoff is derived for both joint and time-orthogonal protocols. For the flat fading, additive, white, Gaussian noise channel, a novel joint communication and estimation scheme using low correlation sequences is presented. It is observed that in most situations, joint communication and estimation performs better than a scheme where communication and estimation are performed individually, furthermore, the gains of joint communication and estimation over individual communication and estimation can be significant as the distortion tolerance increases. Finally, it is observed that even a slight tolerance to errors in the channel parameters close to the theoretical lower bounds yield significant improvements in the rate at which reliable communication is achievable.