Search arXivSearch

arXiv · 1512.04252

OFDM Channel Estimation via Phase Retrieval

Abstract

Pilot-aided channel estimation is nowadays a standard component in each wireless receiver enabling coherent transmission of complex-valued constellations, only affected by noise and interference. Whenever these disturbances are sufficiently small and long data frames are used, high data rates can be achieved and the resource overhead due to the pilots vanishes asymptotically. On the other, it is expected that for the next generation of mobile networks not only data rate is in the main focus but also low latency, short and sporadic messages, massive connectivity, distributed & adhoc processing and robustness with respect to asynchronism. Therefore a review of several well-established principles in communication has been started already. A particular implication when using complex-valued pilots is that these values have to be known at the receiver and therefore these resources can not be used simultaneously for user data. For an OFDM-like multicarrier scheme this means that pilot tones (usually placed equidistantly according to the Nyquist theorem) are allocated with globally known amplitudes and phases to reconstruct the channel impulse response. Phases are designed and allocated globally which is in contrast to a distributed infrastructure. In this work we present therefore a new phaseless pilot scheme where only pilot amplitudes need to be known at the receiver, i.e., phases are available again and can be used for various other purposes. The idea is based on a phase retrieval result for symmetrized and zero-padded magnitude Fourier measurements obtained by two of the authors. The phases on the pilot tones can now be used to carry additional user-specific data or compensate for other signal characteristics, like the PAPR.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Philipp Walk, Henning Becker, Peter Jung. 2015-12-14. OFDM Channel Estimation via Phase Retrieval. https://arxiv.org/abs/1512.04252

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads

Integrated sensing and communication (ISAC) plays a crucial role in 6G, to enable innovative applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilot and random data payload components, poses challenges for target detection due to two reasons: 1) these two components cause coupled shifts in both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in the bistatic setting. Unfortunately, these challenges could not be tackled by existing target detection algorithms. In this paper, a generalized likelihood ratio test (GLRT)-based detector is derived, by leveraging the known deterministic pilots and the statistical characteristics of the unknown random data payloads. Due to the analytical intractability of exact performance characterization, we perform an asymptotic analysis for the false alarm probability and detection probability of the proposed detector. The results highlight a critical trade-off: both deterministic and random components improve detection reliability, but the latter also brings statistical uncertainty that hinders detection performance. Simulations validate the theoretical findings and demonstrate the effectiveness of the proposed detector, which highlights the necessity of designing a dedicated detector to fully exploited the signaling resources assigned to random data payloads.

cs.IT

On Unbiased Parameter Estimation and Signal Reconstruction

In this paper, we extend the theory of depth-unbiased source localization to unbiased parameter estimation and signal reconstruction for an arbitrary number of non-zero parameters. The topic touches on exact reconstructibility, most commonly studied in compressed sensing and multisource estimation across various imaging problems. The theoretical results derive upper bounds on the number of recoverable parameters in the noiseless case, and define a probability measure to assess the likelihood of recovering all non-zero parameters with correct magnitude order. The work provides a mathematical explanation of the open question regarding the noise robustness of standardized and unbiased methods. The paper also reveals a trade-off between the number of sensors and the signal-to-noise ratio. Numerical experiments demonstrate the theoretical findings.

cs.IT

Minimum enclosing Bregman balls made easy

In this work, we revisit the problem of computing minimum enclosing Bregman balls (Bregman MEBs) of finite sets of parameters. First, we show that Bregman MEBs are equivalent to MEBs of corresponding weighted point sets with respect to the power distance. We then report an efficient Frank--Wolfe $(1+ε)$-approximation algorithm for computing power MEBs, for any $ε>0$. This power MEB approximation algorithm coincides with the Bregman MEB approximation algorithm of Nock and Nielsen (2005) when expressed in the dual gradient space. Finally, we show that the Bregman potential lifting transforms used to construct Bregman Voronoi diagrams can be reinterpreted as the classical paraboloid lifting transform applied to corresponding weighted point sets. In particular, Bregman MEB circumcenters lie on the farthest Bregman Voronoi diagrams or equivalently on the corresponding farthest power diagrams.

cs.IT