Deep double-pilot-based hybrid precoding in UAV-enabled mmWave massive MIMO

With the development of remote controlling technique and the emergence of demand in wireless communications, UAV is considered to play an important role in next and beyond next-generation communication system, which brings the possibility of broadband seamless connectivity and flexibility of deployment equipped with multiple antennas and working in millimeter wave band [1]. Compared to conventional terrestrial platforms or high-altitude platforms, UAV-enabled platforms take the advantages of fast deployment, flexible reconfiguration and often more satisfying communication channels because UAV-enabled base station (BS) can dynamically coordinate its position to ensure the presence of short-range line-of-sight links [2]. References [2, 3] summarize three primary kinds of UAV-aided wireless communications, i.e., UAV-aided ubiquitous coverage, UAV-aided relaying, and UAV-aided information dissemination and data collection. The first one is often used to deal with the situation that the terrestrial BS is overloaded or just cannot serve normally due to natural damage where UAV-enabled BS can be flexibly deployed to support the terrestrial BS. The second one is typically used in areas where there are large blocks like mountains or something else to shadow wireless signal significantly, which degrades the performance of mmWave-based communication system [4]. In the last one, UAVs are used to exchange data with a large number of distributed UEs or sensors, in which time delay can be tolerated on a certain level.

MmWave is considered as an opportunity for the communication spectrum in 5G and beyond 5G. With being equipped with massive antennas in both transmitter and receiver, the strong path loss in mmWave can be overcome [5]. However, the massive antennas bring the need for precise channel state information (CSI) with high dimension and the pretty awesome cost in computation and hardware, especially in radio frequency chain (RF chain). Hybrid precoding, as a method to reduce the cost of RF chain and total transmitting power [6], is proposed, while it also needs an efficient algorithm because it is a NP-hard problem due to the constrains in phase shifters. Several efficient algorithms for hybrid precoding in mmWave massive MIMO or normal massive MIMO are proposed including OMP [7], RF iteration [8] and beam steering [9]. In addition, in recent years, more and more researchers pay attentions on the combination between hybrid precoding and deep learning, which often brings an inspiring design and excellent performance due to its powerful ability to mimic almost any relation and function, while we do not need to know what exactly the relation is, just like the hybrid precoding network proposed in [10].

By contrast to the exploring development in time-invariant mmWave massive MIMO, there are not proficient researches according to the time-varying system, such as the scenario of UAV-enabled communications and high-speed railway [11]. Although there are some researches about time-varying, we do a brief summary about existing researches and then introduce the necessity of our research. Reference [12] considers the scenario that the precoder acquired by singular value decomposition (SVD) is not corresponding to the current channel in a time-varying channel model, so the receiver needs to update it with some methods. A time-varying channel estimation method based on the Taylor expansion is presented in [13]. There is also a recent research [14] about time-varying precoding in MIMO-OFDM system, which uses an inverse extrapolation method to settle time-varying precoding problem. However, early researches consider the normal MIMO system and recent researches consider the problem of time-varying channel estimation or full digital precoding rather than hybrid precoding. In the next-generation communication system, it is necessary to research the time-varying hybrid precoding in a mmWave or normal massive MIMO scenario, especially in a UAV-enabled communication system due to the mobility of UAVs, which is both the advantage and new challenge for wireless communication. This is the motivation of our work, and the main contributions of this paper are listed in the following:Notation: We use the following notations throughout this paper: \({\mathbf {A}}\) is a matrix, \({\mathbf {a}}\) is a vector, \(a\) is a scalar and \({\mathbb {A}}\) is a set. \({\mathbf {A}}^ T\) and \({\mathbf {A}}^ H\) are, respectively, the transpose and conjugate transposes of \({\mathbf {A}}\). \(|{\mathbf {A}}|\) is the determinant of \({\mathbf {A}}\), and \(|\mathrm {a}|\) is the absolute value of \(\mathrm {a}\). \(\mathcal {C N}\left( m, \sigma ^{2}\right)\) means a complex Gaussian process with mean \(m\) and covariance \(\sigma ^{2}\). \(\left\| \cdot \right\| _{F}\) is Frobenius norm. \(\circ\) and \(*\) are Hadamard production and convolutional production, respectively.

We consider such kind of scenario in which base station (BS) is deployed on a UAV and both BS and user equipment (UE) adopt the lens model [15], as shown in Fig. 1. It also presents the line-of-sight (LOS) and non-line-of-sight (NLOS) paths of mmWave. The time-varying mmWave channel is based on the time-varying geometry channel model. However, our proposed method is suitable for any hybrid precoding based on the certain kind of codebook. The following subsections will present: (1) the mmWave massive MIMO with lens model and the time-varying channel model and (2) the problem definition of hybrid precoding accompanying with the discussion of time-varying influence, respectively.

In this section, we present the double-pilot-based hybrid precoding system, which is composed of two steps—beam sampling step and prediction step, as Fig. 8, the time axis diagram (the one above), shows. Figure 8 also provides the comparison between the proposed method (the one above) and the common method (the one below)—increasing the sampling rate directly. There are two kinds of pilots with different colors and densities used for, respectively, sampling the equivalent and original channels in the time axis of proposed method, meaning different sampling rates in the aforementioned two steps, which is based on the fact described in Sect. 2. We present the benefits of this double-rate sampling in Sect. 5. Subsection 1 is dedicated to the explanation of beam sampling step and beam prediction step, and subsection 2 explains the digital precoding under having acquired the appreciate beamforming. The ratio of sampling rate of the equivalent channel (yellow pilots) to that of original channel (blue pilots) is notated as \(R _{\mathrm{smpl}}\).

This section presents the deep learning architecture used to predict the beamforming during the period of beam prediction step proposed in the previous section. Also, it is possible to utilize other methods to conduct the beam prediction. It is necessary to note that, for simplification, we use t to replace \(R_{\mathrm{smpl}}t\), and this notation method is just valid within this section because our deep neural network takes part in only the analog precoding so that there is only one kind of sampling rate for it.

Firstly, setting the time relevance length \(L\), the transmitter successively transfers pilot to the receiver in \(L-1\) slots (time slot equals to \(R _{\mathrm{smpl}} T_s\)) to estimate the channel and generate the beamforming selector by selecting the beams which accumulate most power. We get the first \(L-1\) beamforming selectors in beam sampling step according to the previous section, and we can consider a selector matrix as \(N_{RF}\) one-hot labels, which is widely used in classification problems, and this makes the net easy to be trained [21]. One-hot labels are illustrated in Fig. 9.

Here, we adopt the Conv2D LSTM structure to construct our deep learning net due to the assumption that the beamforming vectors for the specific channel are related and beamforming matrices at different times are related as well. Conv2D LSTM combines the characteristics of convolution net and LSTM net (Fig. 10). The previous one takes advantage of grasping the feature spatially, and the post one is good at analyzing time sequence. Conv2D LSTM uses tensor rather than sequence as input and can use former information at the same time. In Fig. 11, we present the structure of Conv2D LSTM block according to [22], which is pretty similar to the common LSTM block, and Fig. 12 explains the counterparts to the three gates in common LSTM block. The blue block in Fig. 11 means convolutional multiplication, which is the most difference between Conv2D LSTM and common LSTM. This structure is also adopted by [23] to predict the downlink CSI.

According to the second reviewer’s comment, we removed the original Fig. 12 and replaced it by the current Fig. 12.

The explicit relationship of the variables in Fig. 11. can be expressed as follows:where the subscripts of \({\mathbf {W}}\) mean the operand according to the specific gate.

Figure 10 illustrates the proposed beamforming index prediction network (BIP-Net) with explicit explanations. \({\mathbf {X}}(t-1)\) in Fig. 10 is the selector \({\mathbf {S}}_t\) or \({\mathbf {S}}_r\) in time \(t-1\) and \(\widehat{{\mathbf {Y}}}(t)\) is the prediction of the beamforming index in time t and can be expressed aswhere \(f_{C 2 L i}(\cdot )\) notes the Conv2D LSTM block i and \(\Phi _{i}\) means the parameters in the previous one. In addition, the red box in the diagram represents 1 in the input matrix and the yellow one means predicted 1 in the output selector.

We use binary cross-entropy as loss function, which in this net can be expressed aswhere y/\({\hat{y}}\) is the element of \({\mathbf {Y}}(t)\)/\(\hat{{\mathbf {Y}}}(t)\), \({\mathbb {P}}=\{\Phi _i |i=1,2,\ldots ,n\}\) is the set of parameters. \({\mathbf {Y}}(t)={\mathbf {X}}(t)\) is label, the precise selector in the next time.

In this section, the simulation results are demonstrated to confirm the feasibility and efficiency of our proposed BIP-Net from the term of achievable rate.

We present the achievable rate of BIP-Net with the research of the influence of time relevance length L in the network. The parameters of our simulated system are as follows. The number of antennas in both transmitter and receiver is 64, i.e., \(N_t=N_r=64\), the RF chain is 3, equals to data streams, and \(P=3\). \(T_s=10\) ms and \(f_{\ell } \in \left[ 0, f_{\max }\right]\), where \(f_{\max }\) can be calculated by Doppler shift formulation. \(R_{\mathrm{smpl}}\) is set to 500 and the velocity is set to 72 km/h, which is an easily achievable speed [24].

Firstly, as illustrated in Fig. 13, the influence of time-varying on normal beamforming methods is significant, where there is a gap between the red line (time-invariant situation) and green line (time-varying situation without BIP-Net). By contrast, with the equipment of BIP-Net, the transmitter and the receiver can transfer messages with almost the same rate of time-invariant situation. Figure 14 takes an example that BIP-Net predicts the correct beamforming, eliminating the influence of time-varying. All these three methods adopt the enumerate way to achieve beamforming due to the fact that we concentrate on the effect of erasing the influence of time-varying rather than beamforming itself. Again, our proposed BIP-Net actually can be leveraged in any beamforming methods to combat the degeneration of the performance as if the methods based on a certain codebook.

Secondly, we take research of the hyper-parameter, the time relevance length L of the Conv2D LSTM, and the result is shown in Fig. 15. Accompanying with the length increasing, the BIP-Net can grasp the precise feature of beamforming indices gradually. In addition, it is also widely known that the process of back-propagation algorithm is slow and hardware-consuming [25], which mobile stations cannot afford. The complicated and large-scale network will extremely constrain the usage of them in different scenarios because developers need to consider any situation and train the network completely in advance. We also execute the experiment of researching the influence of \(T_s\), as Fig. 16 shows.

Finally, it is worth noticing that we use the digital sampling rate during prediction step for the Taylor expansion method, which means that it needs much more pilots for Taylor expansion to achieve this performance since we just need to estimate the equivalent channel rather than the whole channel matrix by our proposed method. What is more, it also brings the huge cost to estimate the channel matrix in the period of beam sampling step with such high frequency. Table 1 explicitly depicts the pilot cost of both of the proposed method and Taylor expansion method.

In this paper, we propose an easy-implementable double-pilot-based using deep learning method in UAV-enabled mmWave massive MIMO, which is suitable in various antenna structures and can be trained pretty fast because we transform the precoding prediction problem into the prediction of the sequence with the end-to-end structure. By exploiting the time correlation of channel matrix, the BIP-Net fits the correlation between beamforming vectors.

In addition, the method is flexible because we do not constrain the specific source of the codebook and different kinds of channel estimation methods, codebook-based hybrid precoding methods and prediction methods can be combined to double-pilot-based hybrid precoding method just with the guarantee of the existence of the temporal correlation between CSI in different times. We believe this ideology of double-pilot-based time-varying hybrid precoding method can improve the performance of UAV-enabled communications or other time-varying communication systems significantly.