Linear Block Coding for Efficient Beam Discovery in Millimeter Wave Communication Networks

Shabara, Yahia; Koksal, C. Emre; Ekici, Eylem

doi:10.48550/arxiv.1712.07161

Cited by 3 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, at any given time t, a beamforming vector w t ∈ W S is selected sequentially as a function of previously observed signals (y t−1 ). We focus on strategies based on posterior matching, where the problem is connected to channel coding over a binary input channel with ([10], [11], [12]) and without ( [13]) feedback. We use the hierarchical posterior matching (hieP M ) scheme from our prior work [8], described in detail in Sect.…”

Section: A Sequential Initial Alignmentmentioning

confidence: 99%

Sequential Learning of CSI for MmWave Initial Alignment

Ronquillo

Chiu

Javidi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

MmWave communications aim to meet the demand for higher data rates by using highly directional beams with access to larger bandwidth. An inherent challenge is acquiring channel state information (CSI) necessary for mmWave transmission. We consider the problem of adaptive and sequential learning of the CSI during the mmWave initial alignment phase of communication. We focus on the single-user with a single dominant path scenario where the problem is equivalent to acquiring an optimal beamforming vector, where ideally, the resulting beams point in the direction of the angle of arrival with the desired resolution. We extend our prior by proposing two algorithms for adaptively and sequentially selecting beamforming vectors for learning of the CSI, and that formulate a Bayesian update to account for the time-varying fading model. Numerically, we analyze the outage probability and expected spectral efficiency of our proposed algorithms and demonstrate improvements over strategies that utilize a practical hierarchical codebook.

show abstract

Section: A Sequential Initial Alignmentmentioning

confidence: 99%

Sequential Learning of CSI for MmWave Initial Alignment

Ronquillo

Chiu

Javidi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

show abstract

“…The problem of mapping the channel measurements to an estimated channel is non-linear and requires high computations. Two different methods to do this mapping were proposed in [13], namely, i) the "look-up table" method and ii) the "search" method. The look-up table method requires storing a table with entries for all possible channel measurements along with their corresponding channels.…”

Section: Measurements Decodingmentioning

confidence: 99%

“…To argue the reliability of DNN-based mapping, we will test it using a simple channel with n t =1, n r =23 and a maximum of 3 available channel paths i.e., L≤3. We also compare the DNN model performance to the "search" method of [13]. Based on the described channel parameters, only m=11 measurements are needed for discovering its paths (more details about this particular example are discussed in Section VII).…”

Section: B Dnn Model Assessmentmentioning

confidence: 99%

“…These initial results are promising but they are obtained using error-free measurements. Now, we will test the resilience of DNN-based mapping against noisy measurements and compare its performance against the search method proposed in [13]. To do so, we generate a new testing data set, in the same way we created the training data.…”

Section: B Dnn Model Assessmentmentioning

confidence: 99%

“…Decoding the obtained measurements can be achieved using a variety of tools including: i) the "look-up" table method, proposed in [13], which is only suitable for transceivers with few-bit ADCs, ii) the "search" method which entails a combinatorial search over the column space of the sensing matrix [13] and iii) the "Approximate Message Passing" (AMP) algorithms [14] and their variants, e.g., GAMP/VAMP [15], among other methods. These methods widely vary in computational complexity and accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Source Coding Based Millimeter-Wave Channel Estimation with Deep Learning Based Decoding

Shabara¹,

Ekici²,

Koksal³

2019

Preprint

Self Cite

View full text Add to dashboard Cite

mmWave technology is set to become a main feature of next generation wireless networks, e.g., 5G mobile and WiFi 802.11ad/ay. Among the basic and most fundamental challenges facing mmWave is the ability to overcome its unfavorable propagation characteristics using energy efficient solutions. This has been addressed using innovative transceiver architectures. However, these architectures have their own limitations when it comes to channel estimation. This paper focuses on channel estimation and poses it as a source compression problem, where channel measurements are designed to mimic an encoded (compressed) version of the channel. We show that linear source codes can significantly reduce the number of channel measurements required to discover all channel paths. We also propose a deeplearning-based approach for decoding the obtained measurements, which enables high-speed and efficient channel discovery.

show abstract

Active Learning and CSI Acquisition for mmWave Initial Alignment

Chiu

Ronquillo

Javidi

2019

IEEE J. Select. Areas Commun.

110

133

View full text Add to dashboard Cite

Millimeter wave (mmWave) communication with large antenna arrays is a promising technique to enable extremely high data rates due to large available bandwidth in mmWave frequency bands. In addition, given the knowledge of an optimal directional beamforming vector, large antenna arrays have been shown to overcome both the severe signal attenuation in mmWave as well as the interference problem. However, fundamental limits on achievable learning rate of an optimal beamforming vector remain.This paper considers the problem of adaptive and sequential optimization of the beamforming vectors during the initial access phase of communication. With a single-path channel model, the problem is reduced to actively learning the Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station (BS). Drawing on the recent results in the design of a hierarchical beamforming codebook [1], sequential measurement dependent noisy search strategies [2], and active learning from an imperfect labeler [3], an adaptive and sequential alignment algorithm is proposed.For any given resolution and error probability of the estimated AoA, an upper bound on the expected search time of the proposed algorithm is derived via Extrinsic Jensen-Shannon Divergence. The upper bound demonstrates that the search time of the proposed algorithm asymptotically matches the performance of the noiseless bisection search up to a constant factor, in effect, characterizing the AoA acquisition rate. Furthermore, the upper bound shows that the acquired AoA error probability decays exponentially fast with the search time with an exponent that is a decreasing function of the acquisition rate.Numerically, the proposed algorithm is compared with prior work where a significant improvement of the system communication rate is observed. Most notably, in the relevant regime of low (−10dB to +5dB) raw SNR, this establishes the first practically viable solution for initial access and, hence, the first demonstration of stand-alone mmWave communication.

show abstract

Linear Block Coding for Efficient Beam Discovery in Millimeter Wave Communication Networks

Cited by 3 publications

References 0 publications

Sequential Learning of CSI for MmWave Initial Alignment

Sequential Learning of CSI for MmWave Initial Alignment

Source Coding Based Millimeter-Wave Channel Estimation with Deep Learning Based Decoding

Active Learning and CSI Acquisition for mmWave Initial Alignment

Contact Info

Product

Resources

About