102,348 research outputs found
Basis expansion adaptive filters for time-varying system identification
No full textElectrical Engineering, Mathematics and Computer Scienc
Frequency Agile Waveform Adaptation for Cognitive Radios
No full textIn the context of cognitive radio (CR) networks, this paper develops a frequency-agile waveform adaptation technique that dynamically adjusts the spectral shape, power and frequency of transmission waveforms for efficient network spectrum utilization. A general framework is presented based on generalized transmitter and receiver basis functions, which allow to jointly carry out dynamic resource allocation (DRA) and waveform adaptation, two procedures that are traditionally carried out separately. New objective function and cognitive spectral mask constraints are formulated for DRA optimization tailored to CR applications. The joint DRA and waveform adaptation approach permits distributed games in multiple access networks, in which participating CR users optimize their respective local utility functions by taking actions from the action space defined by allowable basis function parameters. It results in not only enhanced radio spectrum resource efficiency due to joint optimization, but also affordable complexity scalable in the network size, by virtue of the distributed game approach adopted
Synchronization-Free Data Detection for UWB Communications
No full textThe dense multipath scattering typical of ultra-wideband (UWB) wireless channels provides very large multipath diversity, but from the other side makes the receiver design a demanding task as far as channel estimation and timing recovery are concerned. The contribution of this paper is to derive a novel receiver structure based on the multiple symbols differential detection (MSDD) framework with particular emphasis on bypassing costly channel estimation and relaxing the stringent requirements. imposed on timing recovery. The computational complexity of the proposed detection scheme, that becomes quite impractical as the data block size increases, is then circumvented by resorting to an efficient implementation based on the sphere decoding (SD) algorithm. Simulation results carried out in typical multipath propagation scenarios verify that appealing detection performance is achieved at affordable receiver complexity
Joint Dynamic Resource Allocation and Waveform Adaptation in Cognitive Radio Networks
No full textThis paper discusses the issue of dynamic resource allocation (DRA) in the context of cognitive radio (CR) networks. We present a general framework adopting generalized transmitter and receiver signal-expansion functions, which allow us to join DRA with waveform adaptation, two procedures that are currently carried out separately. Moreover, the proposed DRA can handle many types of expansion functions or even combinations of different types of functions. An iterative game approach is adopted to perform multi-player DRA, and the best-response strategies of players are derived and characterized using convex optimization. To reduce the implementation costs of having too many active expansion functions after optimization, we also propose to combine DRA with sparsity constraints for dynamic function selection. Generally, it incurs little rate-performance loss since the effective resources required by a CR are in fact sparse
A Synchronization-Free Approach to Data Recovery for Multiple Access UWB Communications
No full textUltra-wideband (UWB) wireless channels provide very large diversity, but make the receiver design a demanding task as far as channel estimation and timing recovery are concerned. This paper contributes with a novel receiver structure based on the multiple symbols differential detection (MSDD) framework, which bypasses not only costly channel estimation but also relaxes the stringent requirements imposed on timing recovery. Simulation results carried out in typical dense multipath propagation scenarios verify that appealing detection performance is achieved at affordable complexity thanks to an efficient implementation based on the sphere decoding (SD) algorithm
2-Dimensional finite impulse response graph-temporal filters
No full textFinite impulse response (FIR) graph filters play a crucial role in the field of signal processing on graphs. However,
when the graph signal is time-varying, the state of the art FIR graph filters do not capture the time variations of the input signal.
In this work, we propose an extension of FIR graph filters to capture also the signal variations over time. By considering also
the past values of the graph signal, the proposed FIR graph filter extends naturally to a 2-dimensional filter, capturing jointly the
signal variations over the graph and time. As a particular case of interest we focus on 2-dimensional separable graph-temporal
filters, which can be implemented in a distributed fashion at the price of higher communication costs. This allows us to give filter
specifications and perform the design independently in the graph and temporal domain. The work is concluded by analyzing the
proposed approach for stochastic graph signals, where the first and second order moments of the output signal are characterized
Compressive Sampling based Multiple Symbol Differential Detection for UWB Communications
No full textCompressive sampling (CS) based multiple sym- bol differential detectors are proposed for impulse-radio ultra- wideband signaling, using the principles of generalized likelihood ratio tests. The CS based detectors correspond to two communica- tion scenarios. One, where the signaling is fully synchronized at the receiver and the other, where there exists a symbol level synchro- nization 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 implemen- tation complexity. To resolve the detection of multiple symbols, compressed sphere decoders are proposed as well, for both com- munication scenarios, which can further help to reduce the sys- tem 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
Reduced-Complexity Equalization for MC-CDMA Systems over Time-Varying Channels
No full textWe present a low-complexity equalizer for multicarrier code-division multiple-access (MC-CDMA) downlink systems over time-varying (TV) multipath channels with non-negligible Doppler spread. The equalization algorithm, which is based on a block minimum mean-squared error (MMSE) approach, exploits the band structure of the frequency-domain channel matrix by means of a band LDLH factorization. The complexity of the proposed block MMSE equalizer is linear in the number of subcarriers, and smaller with respect to a serial MMSE equalizer characterized by a similar performance
Compressed Sensing Techniques for Dynamic Resource Allocation in Wideband Cognitive Networks
No full textFor multi-user cognitive networks, joint dynamic resource allocation (DRA) and waveform adaptation techniques have been developed that effectively represent, manipulate and utilize the physical-layer radio resources by synthesizing both transmitter and receiver waveforms from generalized signal expansion functions. To effect distributed DRA games, this paper discusses the intertwined sensing task and develops compressed sensing techniques that simultaneously estimate all the channel and interference links using only a small number of samples collected from a sparse set of expansion functions. By properly identifying and utilizing the sparsity properties of a wideband environment, the proposed schemes considerably reduce both sensing time and implementation costs
Compressive Sampling Based Differential Detection of Ultra Wideband Signals
No full textIn this paper we focus on compressive sampling (CS) based ultra wideband (UWB) differential detection. We formulate an optimization problem to jointly recover the sparse received UWB signals as well as the differentially encoded data symbol. We utilize an alternating direction method of multipliers (ADMoM) to solve this joint optimization problem. Our proposed joint recovery method outperforms the straightforward separate recovery method, which recovers the sparse received UWB signals in a first step and then detects the differentially encoded symbol based on the recovered signals
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