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An adaptive channel estimation algorithm for millimeter wave cellular systems

An adaptive channel estimation algorithm

for millimeter wave cellular systems

LU Wenlü

1,2

, ZOU Weixia

1,2

, LIU Xuefeng

1,2

1. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing 100876, China

2. State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China

Abstract: The large bandwidth available with mmWave (millimeter Wave) makes it a promising candidate for 5th generation

cellular networks. Proper channel estimation algorithms must be developed to enable beamforming in mmWave systems. In this

paper, we propose an adaptive channel estimation algorithm that exploits the poor scattering nature of the mmWave channel

and adjusts the training overhead adaptively with the change of channel quality for mmWave cellular systems. First, we use a

short training sequence to estimate the channel parameters based on the two-dimensional discrete Fourier transform method.

Then, we design a feedback scheme to adjust the length of the training sequence under the premise of ensuring the accuracy of

the channel estimation. The key threshold in the feedback scheme is derived and its inuence on the accuracy of the estimation

results is analyzed. Simulation results confirm that the proposed algorithm can adjust the length of the training sequence

adaptively according to the current channel condition maintaining a stable estimation accuracy.

Key words: millimeter wave, 5G, cellular system, large antenna array, adaptive channel estimation.

Citation: LU W L, ZOU W X, LIU X F, et al. An adaptive channel estimation algorithm for millimeter wave cellular systems[J].

Journal of communications and information networks, 2016, 1(2): 37-44

Journal of Communications and Information Networks

Vol.1, No.2, Aug. 2016

DOI: 10.11959/j.issn.2096-1081.2016.015

Research paper

Manuscript received Jun. 23, 2016; accepted Aug. 3, 2016

This work is supported by The National High Technology Research and Development Program of China (863 Program) (No.2015AA01A703),

The Fundamental Research Funds for the Central Universities (No.2014ZD03-02), The National Natural Science Foundation of China

(No.61571055), fund of State Key Laboratory of Millimeter Wave (No. K201501).

1 Introduction

The data rate requirements of 5G (the next generation)

cellular communication systems will approach or

surpass gigabits per second

[1]

. Such a high data rate

inevitably relies on an enormous available bandwidth

[2]

The huge bandwidth available at mmWave frequencies

makes it one of the best candidates for future 5G

cellular systems

[3-4]

. However, extreme path fading

restricts the propagation distance of mmWave.

Directional beamforming with large antenna arrays

appears to be inevitable to support longer outdoor

links and provide sufcient received signal power

[5]

Moreover, channel estimation must be performed to

obtain the perfect CSI (Channel State Information)

necessary to support the directional beamforming.

However, the channel matrix can be considerably

large in mmWave systems owing to the large number

of antennas, making the classical approach unfeasible

for the estimation of the mmWave channel

[6,7]

Therefore, a major challenge is to develop accurate

and reliable channel estimation algorithms for

Journal of Communications and Information Networks

mmWave cellular systems

[8]

Considering the high cost and power consumption

of devices and enabling multiple data streams, the

hybrid structure of large scale antennas that enable

hybrid analog-digital beamforming have become a

new trend

[2,9]

. Several estimation algorithms for hybrid

structures have been developed. In Refs.[6] and [10],

a novel multiresolution codebook, which is divided

into analog and digital domains, is designed and a

channel estimation algorithm is proposed. In Refs.

[11] and [12], the authors use a compressed sensing

tool to compress the training overhead and propose

corresponding algorithms. A matrix-block algorithm

(DFT-CEA) based on 2D-DFT (Two-Dimensional

Discrete Fourier Transform ) with a short training

overhead to estimate the channel parameters is

proposed in Ref.[7]. All these algorithms can save

the training sequence, however, they cannot adjust

the training overhead adaptively when the channel

condition (such as SNR) changes.

In this paper, considering that the locations

of the MS (Mobile Stations ) relative to the BS

(Base Station) are random and channel qualities at

different locations differ significantly, we develop a

channel estimation algorithm that adjusts the training

sequence length according to the channel condition.

First, the BS sends a short training sequence; after

computing the 2D-DFT of the received signal, the

MS estimates the path parameters iteratively, namely

path gain, AoA (Angle of Arrival), and AoD (Angle

of Departure ). Then, we design a feedback scheme

that allows the MS to distinguish if the estimated

path parameter is noise or a true path by comparing

with a threshold and feedbacks “Yes/No” to decide

whether to continue to send the training sequence. We

emphasize that the proposed first step refers to the

DFT-ACE method in Ref.[7]; the difference is that

we design an adaptive feedback scheme by which

the system adjusts the training overhead. Numerical

results conrm that the proposed algorithm provides

effective performance in mmWave systems.

Notation: A is a matrix, a is a vector, and a is a

scalar. A

, A

, and ||A|| represent the transpose,

conjugate transpose, inverse, and Frobenius norm,

respectively. [y]

a:b

denotes a vector obtained by

extracting elements of vector y from index a to index

b. 0

n×m

denotes a zero matrix of size N×M.

2 System model

We consider the single user mmWave system with

hybrid analog-digital beamforming illustrated in

Fig.1. A BS with M antennas and M

RF (Radio

Frequency) chains communicates with the MS with

N antennas and N

RF chains. The BS transmits M

streams to the MS and the MS obtains N

streams after

processing; in general, N

. According to Refs.[2,13],

we know that M

≤

M and N

≤

We focus on the downlink transmission. The BS is

assumed to apply an M

×M

baseband beamformer

followed by an M×M

RF beamformer F

Similarly, the MS is constituted of an N

×N

baseband combiner W

and an N×N

RF combiner

. To simplify the hardware implementation,

each element of F

and W

has unitary magnitude;

however, it may have an arbitrary phase.

H denotes the N×M channel matrix and x

and x

, where x is the M

×1 vector of the

transmitted symbols. We adopt a narrowband block-

fading channel model and y

at the MS is

, (1)

where is the thermal noise with

variance σ

. The final processed data at the MS is

y=W

Considering the limited scattering nature of the

mmWave channel

[14]

, we adopt a geometric channel

model with L dominant scatterers where each

scatterer is assumed to contribute a single propagation

path between the BS and MS

[2]

. L is a statistics mean

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