This paper is concerned with the channel estimation problem in millimeter wave (mmWave) wireless systems large antenna arrays. By exploiting inherent sparse nature of mmWave channel, we first propose a fast (FCE) algorithm based on novel overlapped beam pattern design, which can increase amount information carried by each measurement compared to existing nonoverlapped designs and thus reduce required time. We develop maximum likelihood estimator optimally extract path from measurements....

Millimeter wave (mmWave) has been claimed to be the only viable solution for high-bandwidth vehicular communications. However, frequent channel estimation and beamforming required provide a satisfactory quality of service limits mmWave In this paper, we propose novel beam tracking framework communications in network setting. For estimation, an algorithm termed robust adaptive multi-feedback (RAF) that achieves comparable performance as existing algorithms, with significantly smaller number...

This paper is concerned with the channel estimation problem in millimetre wave (MMW) wireless systems large antenna arrays. By exploiting sparse nature of MMW channel, we present an efficient algorithm based on a novel overlapped beam pattern design. The performance analyzed and upper bound probability failure derived. Results show that can significantly reduce number required measurements (e.g., by 225% when single overlap used) compared to existing non-overlapped patterns.

This paper is concerned with the channel estimation problem in multi-user millimeter wave wireless systems large antenna arrays. We develop a novel simultaneous-estimation iterative fountain training (SWIFT) framework, which multiple users estimate their channels at same time and required number of measurements adapted to various conditions different users. To achieve this, we represent beam direction process by graph, referred as beam-on-graph, associate code-on-graph decoding problem....

Millimeter-wave (mmWave) communication is a promising technology to meet the ever-growing data traffic of vehicular communications. Unfortunately, more frequent channel estimations are required in this spectrum due narrow beams employed compensate for high path loss. Hence, development highly efficient beam tracking algorithms essential enable technology, particularly fast-changing environments In paper, we propose an innovative scheme based on Extended Kalman Filter (EKF), improving mean...

In this paper, we consider the channel estimation problem in millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting inherent sparse nature of mmWave channel, develop a novel rate-adaptive (RACE) algorithm, which can adaptively adjust number required measurements based on an expected probability error (PEE). To end, design maximum likelihood (ML) estimator to optimally extract path information and associated from increasing measurements. Based ML estimator,...

This paper is concerned with beam tracking problem in millimeter wave communication systems. We use a Markov process to describe the evolution of steering angle, and its temporal correlation characterized by transition probability matrix. A two-stage algorithm, termed probe-then-refine (PTR), proposed. In probing stage, coarse range angle determined via set training beams fixed beamwidth. refining attained further divided using multi-stage hierarchical codebook-based approach. The successful...

Millimeter wave is a promising technology for the next generation of wireless systems. As it well-known its high path loss, systems working in this spectrum tend to exploit shorter wavelength equip transceivers with large number antennas overcome loss issue. The leads channel matrices and consequently challenging estimation problem. algorithms that have been proposed so far either neglect probability error or require feedback overload from receivers ensure target error. In paper, we propose...

This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave sparsity, we propose simultaneous-estimation iterative fountain training (SWIFT) framework, in which average number of measurements is adapted to various conditions. To this end, base station (BS) and each user continue measure random subset transmit/receive beamforming directions until estimate converges. We formulate...

This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave sparsity, we propose simultaneous-estimation iterative fountain training (SWIFT) framework, in which average number of measurements is adapted to various conditions. To this end, base station (BS) and each user continue measure random subset transmit/receive beamforming directions until estimate converges. We formulate...

In this paper, we consider the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting inherent sparse nature of mmWave channel, develop a novel rate-adaptive (RACE) algorithm, which can adaptively adjust number required measurements based on an expected probability error (PEE). To end, design maximum likelihood (ML) estimator to optimally extract path information and associated from increasing measurements. Based ML estimator,...

This paper is concerned with the channel estimation problem in multi-user millimeter wave (mmWave) wireless systems large antenna arrays. We develop a novel simultaneous-estimation iterative fountain training (SWIFT) framework, which multiple users estimate their channels at same time and required number of measurements adapted to various conditions different users. To achieve this, we represent beam direction process by graph, referred as beam-on-graph, associate code-on-graph decoding...

Millimeter wave is a promising technology for the next generation of wireless systems. As it well-known its high path loss, systems working in this spectrum tend to exploit shorter wavelength equip transceivers with large number antennas overcome loss issue. The leads channel matrices and consequently challenging estimation problem. algorithms that have been proposed so far either neglect probability error or require feedback overload from receivers ensure target error. In paper, we propose...

This paper is concerned with the channel estimation problem in multi-cell Millimeter (mmWave) wireless systems. We develop a novel Ray-of-Arrival Passing for Indirect (RAPID) Beam Training framework, which network consisting of multiple BS are able to work cooperatively estimate jointly UE channels. To achieve this aim, we consider spatial geometry mmWave environment and transform conventional angular domain beamforming concepts into more euclidean, Ray-based domain. Leveraging model, then...

Millimeter wave (mmWave) has been claimed to be the only viable solution for high-bandwidth vehicular communications. However, frequent channel estimation and beamforming required provide a satisfactory quality of service limits mmWave In this paper, we propose novel beam tracking framework communications in network setting. For estimation, an algorithm termed robust adaptive multi-feedback (RAF) that achieves comparable performance as existing algorithms, with significantly smaller number...

This paper is concerned with the channel estimation problem in millimetre wave (MMW) wireless systems large antenna arrays. By exploiting sparse nature of MMW channel, we present an efficient algorithm based on a novel overlapped beam pattern design. The performance analyzed and upper bound probability failure derived. Results show that can significantly reduce number required measurements (e.g., by 225% when single overlap used) compared to existing non-overlapped patterns.