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A block diagram for K user mmWave systems with a single RF chain
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In this paper, we consider a multi-user (MU) radio frequency (RF) beam training scenario with a user selection. We propose a new beam training scheme that alleviates the latency issue in the conventional IEEE 802.11ad by adopting a sequential downlink-downlink transmit sector sweep combination. Then, we analyze the average rate performance of sever...
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... Figures 8 and 9, we compare the effective rate performance of various schemes with respect to K for (N t , N r , M t , M r , T ) = (16,16,8,8,200) and (32, 16, 16, 8, 300), respectively. Interestingly, it is observed in both figures that the performance of the SDU increases Proposed SDD Conventional SDU Full search NSS 59% Fig. 9. Effective rate performance with respect to K for (N t , N r , M t , M r , T ) = (32,16,16,8,300) with K up to K = 3 and then it starts to decrease, while that of other schemes increases gradually. This is due to the fact that the beam training latency of the SDU scheme grows with the number of users. Here, we point out that a performance gain of the SDD over the full search scheme shown in Figure 8 and 9 is 34% and 59% at K = 20, respectively. Since the analytical performance gain ? SDD/full for the case of (N t , N r , M t , M r , T ) = (16,16,8,8,200) and (32,16,16,8,300) is given by 1.35 and 1.60, respectively, we can conclude that the our asymptotic analysis is valid regardless of N t and M t even for a small ...
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... illustrated in Figure 1, we consider an MU RF beam training scheme in mmWave systems where the BS and K users are equipped with N t and N r antenna elements, respectively. We assume that the BS and all users employ a single RF chain due to the high cost issue of the RF circuit in practical mmWave systems [6]. Thus, in this paper, we restrict our discussion to the analog RF beamforming. In this case, the BS can support only one user at a time by an RF beamforming ...
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Citations
... In [75], another two stage search is proposed by IEEE 802.11ad standard which decides the possible beam at the user and the BS sequentially by adopting the Sequential Downlink-Uplink (SDU) transmit sector sweep combination. In [76], the authors extend the application scenario to the MU-MIMO case which adopts a Sequential Downlink-Downlink (SDD) transmit sector sweep combination. ...
Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, highly directional beams are exploited to achieve robust links by hybrid beamforming. Accurately aligning the transmitter and receiver beams, i.e. beam training, is vitally important to high data rate transmission. However, it may cause huge overhead which has negative effects on initial access, handover, and tracking. Besides, the mobility patterns of users are complicated and dynamic, which may cause tracking error and large tracking latency. An efficient beam tracking method has a positive effect on sustaining robust links. This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation (6G) mobile network. Especially, some open research problems are proposed to realize fast, accurate, and robust beam training and tracking. We hope that this survey provides guidelines for the researchers in the area of mmWave communications.
... Therefore, the required beam training complexity for finding analog beamformers is O(KN p |C r |). Also, we compare two conventional beam training methods which are sequential downlink-downlink (SDD) method proposed in [22] and beam training scheme proposed in IEEE 802.11 ad [23]. ...
... In Fig. 4, beam training complexities according to size of BS codebook are shown in case of K = N RF = 8. As the size of BS codebook is increased to improve system performance, the beam training complexities of the all conventional schemes is increased [7] [22] [23] [24]. The beam training complexities of the proposed scheme, however, are constant regardless of the size of BS codebook. ...
Millimeter-wave (mmWave) communication with a large bandwidth can result in a significantly improved data rate in wireless communications. To overcome high path-loss in the mmWave frequency band, beamforming technology is necessary. Especially, there has been widespread interest in development of hybrid beamforming (HB) technologies, in view of reducing cost and power consumption in massive multiple input multiple output (MIMO) systems. Some of existing researches on HB algorithms assumed perfect channel state information (CSI) and the others used beam training process in case of assuming imperfect CSI. When beam training process is used, enough beam training has to be conducted to achieve sufficient system performance in massive MIMO systems, which results in significant training overhead. Thus, it is necessary to reduce beam training complexity. Compared to state-of-the-art technology, we propose a multi-user HB system using codebooks based on a deep neural network (DNN) in this paper. In our proposed scheme, beam codewords for the base station (BS) and all users can be inferred using limited beam training in cases when the channel state information (CSI) is unknown. In order to apply the proposed scheme to situations where the CSI is unknown, reference radio frequency (RF) beamformers were introduced. Also, the proposed DNN structure is designed considering introduced reference RF beamformers. By using the proposed DNN with reference RF beamformers, the proposed system can inferred optimal beam codewords with limited beam training. Results obtained from simulations indicate that the proposed scheme can achieve almost the same performance as a conventional scheme with less beam training complexity. We also show that the performances achieved by the proposed scheme are gradually increased as training epoch is increased, eventually converging to a steady-state value.
This paper studies a reinforcement learning (RL) approach for beam tracking problems in millimeter-wave massive multiple-input multiple-output (MIMO) systems. Entire beam sweeping in traditional beam training problems is intractable due to prohibitive search overheads. To solve this issue, a partially observable Markov decision process (POMDP) formulation can be applied where decisions are made with partial beam sweeping. However, the POMDP cannot be straightforwardly addressed by existing RL approaches which are intended for fully observable environments. In this paper, we propose a deep recurrent Q-learning (DRQN) method which provides an efficient beam decision policy only with partial observations. Numerical results validate the superiority of the proposed method over conventional schemes.
In this paper, we study a beam tracking problem for millimeter wave (mmWave) channels in massive multipleinput and multiple-output communication systems. We formulate the beam tracking problem as a stochastic filtering problem by modeling the dynamic state equation and the observation equation. Then, given prior information of the channel, we propose an efficient grid-based Bayesian beam tracking algorithm by leveraging the sparsity of the mmWave channel. Our proposed algorithm examines neighboring transmit and receive beam pairs and exploits multiple observations on the angular domain representation in the sense of the Bayesian statistics. Numerical results show that the proposed Bayesian method with multiple observations outperforms conventional schemes in terms of both the beam tracking accuracy and computational complexity.
Millimeter-wave (mmWave) frequency bands, which offer abundant underutilized spectral resources, have been explored and exploited in the past several years to meet the requirements of emerging wireless services highlighted by high data rates, ultrareliability, and ultralow delivery latency. Yet, the unique characteristics of mmWave, e.g., continuous wide bandwidth, large path, and penetration losses, along with hardware constraints, call for innovative technologies for mmWave communication. Recently, an extensive amount of work on mmWave communication has been carried out by researchers and practitioners from both academia and industry, and various technologies have been developed for mmWave communication systems to fulfill the full potential of mmWave frequency bands. In this article, we present a comprehensive survey of the standardization of mmWave communication, the latest progress and outcomes of the research on mmWave communication technologies, and the emerging applications of mmWave communication. In particular, we provide a timely and in-depth summary of the state-of-the-art technology specifications of mmWave communication with an emphasis on the physical (PHY) layer. Then, we elaborate on a number of well-established or promising antenna architectures in mmWave communication systems and investigate the enabling PHY layer transmission technologies. Finally, we show some existing and emerging applications of mmWave communication and discuss the potential open research issues.
In this paper, we propose a novel hybrid beamforming design algorithm in multi-user massive multiple-input-multipleoutput millimeter-wave channels. In the proposed structure, we employ variable radio frequency attenuators at each phase shifter which flexibly controls the transmit signal amplitude to achieve the performance of fully digital (FD) precoders. We formulate a power loss minimization problem which can be easily solved via the alternating optimization process. Simulation results demonstrate that the proposed architecture achieves the same performance as the FD scheme with the reduced number of radio frequency chains and the proposed scheme exhibits a 37.4% power gain over conventional methods.
Combined with massive multiple-input multiple-output technology, beamforming can effectively compensate the high path-loss of millimeter-wave signals and hence improve the received power at user equipment (UE). In order to establish the beam alignment between base station (BS) and UE, beam training (BT) is required to select appropriate beamforming vectors at both BS and UE sides. Generally, good trade-off between beamforming gain and BT overhead is critical to enhance the system performance. In this letter, a statistics-assisted beam training (SA-BT) scheme is proposed to reduce the BT overhead. Specifically, BS scans the candidate beams according to the descending order of the utilization frequency of each beam. Meanwhile, BT procedure is finished when the beamforming gain at UE exceeds the pre-defined threshold. Theoretical analyses and numerical simulations prove that the proposed scheme can reduce the BT overhead and hence improve the spectral efficiency significantly compared to the existing methodologies.
