Compressive Sampling-Based Multiple Symbol Differential Detection for UWB Communications

Abstract

Abstract-Compressive sampling (CS) based multiple symbol differential detectors are proposed for impulse-radio ultrawideband signaling, using the principles of generalized likelihood ratio tests. The CS based detectors correspond to two communication scenarios. One, where the signaling is fully synchronized at the receiver and the other, where there exists a symbol level synchronization only. With the help of CS, the sampling rates are reduced much below the Nyquist rate to save on the high power consumed by the analog-to-digital converters. In stark contrast to the usual compressive sampling practices, the proposed detectors work on the compressed samples directly, thereby avoiding a complicated reconstruction step and resulting in a reduction of the implementation complexity. To resolve the detection of multiple symbols, compressed sphere decoders are proposed as well, for both communication scenarios, which can further help to reduce the system complexity. Differential detection directly on the compressed symbols is generally marred by the requirement of an identical measurement process for every received symbol. Our proposed detectors are valid for scenarios where the measurement process is the same as well as where it is different for each received symbol. Index Terms-Compressive sampling (CS), multiple symbol differential detection (MSDD), sphere decoding (SD), ultra-wideband impulse radio (UWB-IR).

Compressive Sensing Techniques for Next-Generation Wireless Communications

“…2) Larger transmission bandwidth may be invoked relying on both CR [7], [8] and UWB [9], [10] techniques, both of which can coexist with licenced services under the umbrella of spectrum sharing, where the employment of sub-Nyquist sampling is of salient importance. As another promising candidate, mmWave communications is capable of facilitating high data rates with the aid of its wider bandwidth [1], [11], [12].…”

Narrow Band Interference Elimination based on Compressed Sensing in UWB Energy Detector

“…Novel differential detection method 17 is proposed which exploits CS framework and optimisation problem is formulated to jointly reconstruct the sparse signal and differentially encoded data. The differential detection method proposed is further extended for multiple symbols using generalised likelihood ratio tests 18 . Methods for channel estimation are provided for CS based UWB communication, time delay estimation is provided [19][20][21] .…”

Compressed-Domain Detection and Estimation for Colocated MIMO Radar

“…For instance, CSP is used in [36] to detect sparse signals in additive white Gaussian noise and estimate the degree of sparsity. Similarly, a CSP-based symbol detector for ultrawideband communications is proposed in [37]. Also, CSP is used in [38] to accomplish joint compressive single target detection and parameter estimation in a radar.…”