Distributed Real-Time Implementation of Interference Alignment with Analog Feedback

Lee, Seogoo; Gerstlauer, Andreas; Heath, Robert W.

doi:10.1109/tvt.2014.2357391

Cited by 10 publications

(13 citation statements)

References 24 publications

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“…Pilot transmission require adequate resources to be assigned, and in certain circumstances, to CSI feedback increasing the readiness of precise CSI. Subsequently, IA precoders need be recalculated during an appreciably change in channel conditions, in high-mobility fast-fading networks, the overhead of CSI acquisition can reduce the gains of IA [133]. Hence, minimal overhead signalling dimension techniques need be developed for an appropriately trade-off between CSI quality and CSI acquisition overhead.…”

Section: Channel State Information Knowledge and Feedbackmentioning

confidence: 99%

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Abdulkadir

Simpson

Sun

2019

JSAN

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Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.

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Section: Channel State Information Knowledge and Feedbackmentioning

confidence: 99%

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Abdulkadir

Simpson

Sun

2019

JSAN

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“…Without the master-slave approach, the implementation difficulty is overwhelming [27,28]. The efforts spent on multi-user testbeds are worth the time, budget, and resources since engineers and scientists can gain invaluable practical experiences while publishing academic journals [7,13].…”

Section: Multi-user Testbedsmentioning

confidence: 99%

“…A hybrid design where hardware accelerated approach and highlevel tools, e.g., LabVIEW, are combined is an implication [18] that IA implementation is on its path to hit the market. At this point, experts on embedded designs can collaborate with IA experts that will lead ASIC implementations of IA [7]. Possibly due to the increased costs and less likely of having mobile devices with multiple antennas in the very near future, so far all existing IA testbeds consider single stream transmission.…”

Section: Mimo Beamformingmentioning

confidence: 99%

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Implementation aspects for interference alignment

Yetis

Anand

Kayran

et al. 2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

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Several interference alignment (IA) testbeds have been reported by research groups that validate the theoretical findings thus demonstrating the feasibility of IA. The design complexity of the IA testbed is significant since many simultaneous wireless links have to be established to observe the benefits of IA. Therefore, the already enormous efforts needed for the development of multiple-input multiple-output (MIMO) testbeds are folded multiple times in IA testbeds. In general, testbeds can be categorized into three groups depending on the hardware platform, generic, specific and the hybrid of two. Clearly, developing on generic, hybrid and application specific platforms in turn are the safe steps towards real life prototyping. There are many research groups that have been developing IA on generic platforms [1]- [17]. To the best of our knowledge, there is only one research group that has developed on a hybrid platform [18], and none on the application specific platform. In this paper, we review the experimental IA evaluations in the literature, include important aspects from well-known prototyping principles in the picture, and give comparative discussion with the state-of-art wireless communications testbeds.

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“…Such requirements are also mentioned in [3] as the main challenge for distributed beamforming to work in the next generation wireless communication systems. Similarly, in [4]- [6], accurate time synchronization is shown to be a requirement for interference alignment to work. Emerging concepts like femto-cells pose more challenging synchronization requirements in terms of scalability and accuracy as discussed in [7].…”

Section: Introductionmentioning

confidence: 99%

Scalable and Passive Wireless Network Clock Synchronization in LOS Environments

Zachariah

Dwivedi

Händel

et al. 2017

IEEE Trans. Wireless Commun.

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Abstract-Clock synchronization is ubiquitous in wireless systems for communication, sensing and control. In this paper we design a scalable system in which an indefinite number of passively receiving wireless units can synchronize to a single master clock at the level of discrete clock ticks. Accurate synchronization requires an estimate of the node positions. If such information is available the framework developed here takes position uncertainties into account. In the absence of such information we propose a mechanism which enables simultaneous synchronization and positioning. Furthermore we derive the Cramer-Rao bounds for the system which show that it enables synchronization accuracy at sub-nanosecond levels. Finally, we develop and evaluate an online estimation method which is statistically efficient.

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Distributed Real-Time Implementation of Interference Alignment with Analog Feedback

Cited by 10 publications

References 24 publications

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Implementation aspects for interference alignment

Scalable and Passive Wireless Network Clock Synchronization in LOS Environments

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