Scheduling of UAV-assisted Millimeter Wave Communications for High-Speed Railway

To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not be directly transmitted from the track-side BS. In this paper, we propose an UAV-assisted scheduling scheme which utilizes a UAV to serve as relay for such flows. The proposed scheme also utilize two mmWave bands, one for the BS links and the other for the UAV links. The proposed algorithm aims to maximize the number of flows with their QoS requirements satisfied. Simulations demonstrate that the proposed scheme achieves a superior performance on the number of completed flows and the system throughput over two baseline schemes.


I. INTRODUCTION
With the rapid development of high-speed railway (HSR) in recent years, there is a compelling need for high-quality broadband wireless access services for HSR. There is increasing demand to support data-and bandwidth intensive applications (e.g., multimedia applications) for HSR passengers. The current transmission rate requirement of each train cabin is about 37.5 Mbps according to a recent study, while may continue to grow and reach 0.5 ∼ 5 Gbps in the future [1]. Obviously, these current transmission schemes cannot meet such increasing demands.
To this end, the millimeter wave (mmWave) band from 30 GHz to 300 GHz has abundant spectrum resources and can provide multi-gigabit transmission rates for bandwidthintensive services, such as high-speed data transmission between devices (such as cameras, smartphones, tablets and laptops), compressed and uncompressed high-definition TV, real-time playback, wireless gaming, wireless personal area network, cellular access and wireless backhaul, etc. [2]- [5].
In this paper, we consider the mmWave communications between the HSR and the ground base station (BS). To overcome the severe penetration loss of the train body shell, multiple mobile relays (MRs) are deployed on the rooftop of the train to provide better connections to the track-side BS. The passengers in the train communicates with the BS through radio access links, using the MRs as relays. MRs serve passengers via the APs which are installed in each carriage to avoid the penetration loss and frequent handover. Note that, the communications inside the train can use multiple radio access technologies, such as LTE or WiFi. In fact, the links between the MRs and the track-side BS are the main cause of the capacity bottleneck [6]. Thus, we focus our work on the mmWave communications between the BS and MRs.
In the typical scenario of mmWave HSR systems, there are a certain number of traffic flows to be transmitted. Each flow has its own minimum throughput requirement (i.e., the lowest transmission rate requirement), which is also referred to as its quality of service (QoS) requirement in this paper. Due to the diversity of applications, the QoS requirements of different flows are diverse. Furthermore, the mutual interference from concurrent link transmissions and the presence of eavesdroppers may cause some flows fail to be scheduled directly [7]. Therefore, we propose to deploy a UAV to relay the flows that cannot be directly transmitted from the BS to MRs under certain conditions. We present an UAV-assisted scheduling algorithm that exploits two mmWave bands (including one lower band for links from the BS and a higher band for links from the UAV) to achieve high system throughput. We also consider protecting the mmWave communications from potential eavesdroppers by enforcing the security capacity constraint. The main contributions of this paper are summarized as follows.
• To increase the network capacity of the HSR communication system and satisfy the needs of HSR passengers, we exploit the available spectrum resources of two mmWave frequency bands for the scheduled flows with diverse QoS requirements. The lower one of the two mmWave frequency bands is used for links from the BS, and the higher one is used for links from the UAV. Besides, we consider the existence of potential eavesdroppers, we determine whether a flow can be scheduled based on the security capacity to ensure its communication security. • Due to interference, eavesdropping, or other problems, some flows cannot be directly scheduled from the BS to the MRs. To address this issue, we propose to use a UAV as a relay. With the assistance of the UAV, there is more flexibility for scheduling the flows. We formulate the problem of optimal scheduling as a nonlinear integer programming problem. Moreover, we propose a heuristic UAV-assisted scheduling algorithm to maximize the number of flows with their QoS requirements satisfied within a fixed time. • We evaluate the proposed UAV-assisted scheduling scheme for the mmWave HSR network with simulations. Compared with two baseline schemes, the simulation results demonstrate that the proposed scheme achieves a superior performance on both the number of completed flows and the system throughput. The remainder of this paper is organized as follows. In Section II, we review the related work. Section III presents the system model and assumptions. In Section IV, we formulate the optimal scheduling problem as a nonlinear integer programming problem. In Section V, we propose the relay decision algorithm and UAV-assisted scheduling algorithm. Section VI presents our simulation study of the proposed scheme. We conclude this paper in Section VII.

II. RELATED WORK
There are significant efforts on exploiting mmWave communications for augmented capacity for HSR systems. For the problem of flow forced interruption, utilizing other receivers/nodes as a one-hop or multihop relay to change the original transmission path is the most common solution [8]- [10]. In this paper, we do not use any original nodes to relay blocked flows, but use the UAV to create a new, high-altitude relay node. Due to the superiority of the UAV's open location, superior relay effects can usually be achieved. Many prior works use alternative APs, which are densely deployed, to substitute direct paths from transmitter to receiver [11]- [14]. In this work, we adopt alternative APs in the HSR system, i.e., the MRs are deployed on top of the train to assist transmissions from the BS to passengers in the train.
There have been considerable prior works on scheduling in mmWave networks. Time division multiple access (TDMA) based schemes are commonly used in mmWave networks. Such schemes consider different types of link transmissions, including radio access, backhaul, and so on [15]- [18]. At present, many existing schemes aim to satisfy the QoS requirements of flows, where the scheduling scheme is designed under the premise of meeting the QoS requirement of each flow, and aiming to finally achieve different optimization goals [19], [20]. In this work, the goal is to develop a QoSaware solution to schedule flows in HSR systems, which is assisted by an UAV relay. We consider the cooperation of two mmWave bands to achieve greater transmission capacity. We also consider the corresponding security measures against the communication security risks from potential eavesdroppers.
Recently, there have been a few studies on UAV-assisted relay networks [21]. The scheme of the optimal UAV placement is proposed for the single user or multiple users in the maritime communication, which can increase the capacity of wireless backhaul link between BS and UAV [22]. A cooperative secrecy transmission mechanism is presented for multiple UAV assisted relay selection to maximize the secrecy capacity [23].
In this paper, the transmission research is in HSR systems with the specific communication environment, and the completed link number and system throughput are maximized by scheduling link transmissions. UAV can serve as an energy source and provide RF energy for low power device-to-device (D2D) pairs [24]. Moreover, UAV enables the simultaneous wireless information and power transfer (SWIPT) system by jointly optimizing time switching-based relaying (TSR) and the optimization of its hovering location for better channel quality to maximize the network throughput [25]. These works usually assume the Rician fading channel as well as the lineof-sight (LoS) path between the UAV and each receiver [26]. To better model the actual channel state in HSR systems and to provide a better fit to real measurements observed in LoS scenarios, the κ-µ distribution was proposed in [27]- [30] as a general fading channel model where the Rician, Nakagamim, and Rayleigh distributions are its special cases. In this paper, we adopt the α-β path loss model which is more in line with the path loss measurements in the HSR scenario. Besides, there are works which have studied the application of UAVassisted communications in HSR system. Two UAV-assisted relaying transmission schemes are presented to allocate limited power for moving source and UAV, which are to save signaling overhead during the entire transfer process [31]. In this work, we proposed QoS-aware solution to maximize the number of scheduled flows. In addition, we allocate the time resource for two types of links and consider the cooperation of two mmWave bands for the links from BS to UAV and the links from UAV to MRs.
It is worth noting that these existing related works have not considered utilizing UAV to relay traffic flows which cannot be directly transmitted from the BS to MRs. In this paper, we integrate the dual-band cooperation of mmWave communications into the scheduling problem, aiming to maximize the number of scheduled flows. We also aim to satisfy the QoS requirements of each flow and avoid the potential risks caused by eavesdroppers. We will show through analysis and simulation that the proposed scheme can achieve a superior performance compared to the baseline schemes.

A. System Model
In this paper, we consider a single cell scenario of the mmWave HSR communication system, as shown in Fig. 1. A fixed BS is located beside the railway track, and a UAV is hovering above at a fixed position within the coverage of the track-side BS.
which are within the coverage range of the BS and UAV. The BS links are on frequency band f 1 , indicated by the black arrows in Fig. 1. The communications between the UAV and MRs are on frequency band f 2 , which are shown as red arrows in the figure. In addition, there is an eavesdropper, termed Eva, which is trying to overhear the communications. Without loss of generality, we assume Eva can only eavesdrop on the BS through frequency band f 1 .
There are F traffic flows between the BS and MRs, which need to be scheduled in the system. The UAV provides relay service in the air, thus each flow may be received from the BS directly or be relayed by the UAV for the destination MR. MRs can communicate with the track-side BS or the aerial UAV through radio access links. According to the selection of the transmission methods of the flows, some links are scheduled for transmission each time. In this paper, a link represents the actual connection of direct communications between two endpoints in the system.
In this paper, we consider the scenario where a train runs at a constant velocity v = 300km/h. In this case, the speed of the train is in line with reality. Although the train is moving at a high speed, the large bandwidth of mmWave allows the train to receive a large amount of data in a very short time. Therefore, the pressure of handover problem in high-speed railway communication can be greatly alleviated. We consider the downlink transmissions. The auxiliary transmission of UAV can effectively alleviate the link blockage problem caused by obstacles on the ground. All the devices in the system are assumed to operate in the half-duplex mode, i.e., they can only transmit or receive data at a time. Each MR is equipped with one steerable directional antenna, so it can only receive content from the BS or UAV at a time. The UAV is equipped with one directional antenna for receiving and N Ut antennas for transmitting. The BS is equipped with N Bt directional antennas for transmitting. In the proposed system, time is divided into a series of non-overlapping super frames. Each super frame consists of a scheduling phase and a transmission phase. The scheduling phase is the time duration to collect link requests and their QoS requirements. The transmission phase consists of M equal time slots (TS). For clarity of exposition, we use TSs to measure the transmission time. For reliable transmissions, the control messages and BS requests can be transmitted with 4G technologies, including the source/destination information and the QoS requirements of each link [32]. The proposed scheduler is to allow multiple links be transmitted concurrently using spatial-time division multiple access (STDMA) [33].

B. Antenna Model
As in prior works, directional antenna is necessary for mmWave communications [34]. We assume that the BS and UAV are both equipped with multiple directional antennas, which means they can send to multiple links simultaneously. However, each MR can receive from only the BS or UAV at a time. We adopt multiple antennas to form multiple directional beams at the BS and UAV for transmitting multiple data flows simultaneously [35]. The maximum numbers of beams formed simultaneously at the BS and UAV are limited by the numbers of theirs equipped transmitting antennas.
In this paper, we adopt the realistic directional antenna model which is mentioned in [36]. All directional antennas can steer their beams for the maximum directivity gain [37], [38]. The perfect beam alignment for the desired link has the maximum directivity antenna gain of G 0 . The antenna model can be expressed as where θ is an angle within the range [0 • , 180 • ]. G 0 is the maximum antenna gain, G 0 = 10 log(1.6162 sin(θ −3dB /2)) 2 . θ −3dB is the angle of the half-power beamwidth. θ ml is the main lobe width, θ ml = 2.6 · θ −3dB . G sl is the sidelobe gain, The directional antenna model is for the antenna gain calculations of the links.

C. Transmission Model
The directional path loss in the target environment is studied via the impacts of antenna beam and propagation conditions. After evaluating the different existing models, the α-β model is selected as the path loss model for quantitative analysis in this work [39]. For a link i, the path loss between its transmitter t i and its receiver r i is where α and β are constants, d tiri is the distance between t i and r i , and X σ is a zero mean Gaussian random variable with a standard deviation σ.
Compared with line-of-sight (LOS) transmissions, non-lineof-sight (NLOS) transmissions suffer from much higher attenuation in the mmWave band [40]. Therefore we only consider the directional LOS links. For a link i, the received signal power at receiver r i from transmitter t i is where G ti is the transmitter antenna gain of link i, G ri is the receiver antenna gain of link i, P t i is the transmit power at the transmitter of link i, and P L(d tiri ) is calculated by (2). Since we allow multiple links transmit concurrently by the STDMA transmission scheme, there is mutual interference between concurrent links using the same frequency band. For co-band concurrent links i and j, the interference at r i from t j can be calculated as where G tj is the antenna gain at the transmitter of link j, G ri is the antenna gain at the receiver of link j, which is calculated as in (1), P t j is the transmit power at the transmitter of link j, and P L(d tj ri ) is calculated by (2). According to Shannon's formula, the transmission rate of link i is given by where η describes the efficiency of the transceiver design, which in the range of (0, 1); W i is the transmission bandwidth of link i; and N 0 is the one-sided power spectra density of the white Gaussian noise. We denote R t i as the actual transmit rate of link i in time slot t, which is calculated by (5). Then the achieved throughput of link i in this time slot can be obtained as where T s is the duration of the scheduling phase, and ∆T is the duration of each TS. The denominator in (6) is the duration of a super frame.

D. Secrecy Capacity Model
In this paper, we consider the existence of an eavesdropper, which can overhear the BS transmissions on frequency band f 1 . It is important to ensure secure communications and prevent information leakage. The secrecy capacity, an important physical layer security performance metric, is adopted to determine the feasibility of the schedule scheme in this work. Let C M and C E denote the Shannon capacities of the main channel (C M = R i if link i is attacked) and the eavesdropping channel, respectively. The secrecy capacity is given by [41] where C M and C E are both calculated by (5). The position of the eavesdropper is constantly changing with the train moving. But the eavesdropper is fixed in a certain position of the train and is stationary relative to the train. Hence, compared with other randomly moving eavesdroppers, the location of the eavesdropper in this case is easier to estimate. The eavesdropper is not completely passive, and sends eavesdropping messages to the unknown receiver. The train is slow when it starts to run and can be regarded as stationary. At this time, we adopt the angle of arrival (AOA) positioning method to determine the specific location of the eavesdropper on the train [42]. The BS and MRs detect the signals sent by the eavesdropper to obtain the angles of incidences. Then the location of the eavesdropper can be determined by these angles of incidences. However, determination of the eavesdropper location facilitates the calculation of the security capacity.

IV. PROBLEM FORMULATION AND ANALYSIS A. Problem Formulation
In this paper, the goal is to accommodate as many flows as possible through effective scheduling. In fact, different flows carry different types of service. Thus each flow f has its QoS requirement q f in the form of its required minimum throughput. We define a binary variable δ f to indicate whether flow f is scheduled successfully. If that is the case, we have δ f = 1; otherwise, δ f = 0. Let there be F flows. Then the objective function of the scheduler is given by For link i, a binary variable a t i is defined to indicate whether the link is scheduled for transmission in time slot t. If so, a t i = 1; otherwise, a t i = 0. For the links generated from relayed flows, they are all transmitted to or received from the UAV. The following constraint enforces the half-duplex transmission condition.
The constraint means the link to the UAV and the link from the UAV can't be scheduled concurrently. Each MR and the UAV can only receive a single link at a time. In other words, the receivers of any two concurrent links i and j must be different. Thus, we have the following constraint.
Each link i has its QoS requirement q i . The actual achieved throughput of each flow must satisfy its QoS requirement. For link i, the QoS constraint is as follows.
Regardless of whether it be the BS or UAV, the numbers of links can be simultaneously transmitted are limited by the numbers of its transmit antennas. In time slot t, the set of links transmitted from the BS is defined as S t B , and the set of links transmitted from the UAV is defined as S t U . Then we have the following hardware constraint.
Some flows are relayed by the UAV and then forwarded to the target MRs. The total throughput of the links transmitted from the BS is larger than the sum throughput of the links transmitted from the UAV.
where q a i and q a j are calculated as in (6). Due to the existence of an eavesdropper, the confidentiality of transmissions must be guaranteed. The eavesdropping channel between a legitimate pair is a degraded version of the main channel [41]. In time slot t, the main channel capacity and the eavesdropping channel capacity of link i are denoted by C i M and C i E , with the following constraint [43].
The problem of maximizing the amount of scheduled flows in in each time slot can be formulated as follows.
It can be seen that Problem P1 is a nonlinear integer programming problem. There are also complex nonlinear terms in the constraints. The problem is actually NP-hard [19], [44]. Thus, we aim to develop heuristic algorithms to obtain competitive solutions with low computational complexity.

V. THE UAV-ASSISTED SCHEDULING SCHEME
In this section, we propose an UAV-assisted scheduling scheme. With this scheme, each flow can choose from two transmission methods: one is to directly transmit from the BS to the target MR, and the other is to use the UAV as a relay. The proposed scheme first determines the transmission method for each flow and then schedules the links to transmit, aiming to maximize the number of scheduled flows.

A. The Relay Decision Algorithm
The relay decisions of the flows are based on their QoS requirements and the transmission efficiency. The flows that need to be scheduled are determined based on the transmitterreceiver ranges and their QoS requirements. Then we schedule the flows in each time slot for transmission.
We first consider whether the QoS requirements of the flows can be satisfied. Then we make a selection for each flow between being directly transmitted and being relayed. The set of the links which select to be directly transmitted from BS to MR is defined as S 1 , and the set of the links relayed by the UAV is defined as S 2 .
For each flow f , if it is directly transmitted from the BS to MR in the f 1 band, then this decision will allow one link l 1 from BS to MR to be scheduled. The achieved throughput q a l1 of this link is computed as in (6). If flow f is to be relayed by the UAV in the f 2 band, then there are two links l 2 and l 2 to be scheduled: one from the BS to UAV and the other from the UAV to MR. The achieved throughput of these two links, q a l2 and q a l 2 , can be calculated by (6). Since it is uncertain which links are transmitted simultaneously, the above throughput calculated by (6) does not consider the interference from other links. We can judge whether the two decisions (relay and no relay) can successfully transmit the flows by comparing the calculated throughputs and the QoS requirements. For flow f , the choice between direct transmission and relayed transmission can be decided as follows.
1) If min{q a l1 , q a l2 , q a l 2 } ≥ q f , flow f can be scheduled by both choices.
2) If q a l1 ≥ q f and min{q a l2 , q a l 2 } < q f , flow f can only be directly transmitted from the BS.
3) If q a l1 < q f and min{q a l2 , q a l 2 } ≥ q f , flow f need to be relayed by the UAV. 4) If q a l1 < q f and min{q a l2 , q a l 2 } < q f , flowf should be abandoned. It should not be considered in the later scheduling process. Since the eavesdropper is stealthily listening on the BS transmissions in the f 1 frequency band, the confidentiality of transmission should be considered. The location of the UAV ensures that the BS-UAV links can satisfy constraint (14). Thus the flows relayed by the UAV are less likely of being eavesdropped. For flows which are directly transmitted from the BS in the f 1 frequency band, we judge whether a direct transmission is feasible by evaluating (14).
To further compare the two transmission choices, we make a preliminary estimation of the transmission time of all the flows in S all = S 1 ∪ S 2 . The number of estimated time slots of flow f can be obtained as follows.
Here R i is the estimate rate without considering other links' interference at this time. The remaining flows select a better transmission choice by comparing the number of TSs required by the schedule. The relay decision algorithm is summarized in Algorithm 1. First, we remove the flows whose QoS requirements cannot be satisfied by either of the two transmission choices in Line 2. In addition to the flows in set D , the other flows are divided into three categories based on whether their QoS requirements can be satisfied under different transmission choices in Lines 5-24. In Lines 5-6, the flows with their QoS requirements satisfied by being relayed by the UAV are added to S 2 . In Lines 9-13, the transmission methods for the other flows are determined, with their QoS requirements satisfied by direct transmission from the BS and satisfying the secrecy capacity condition. And these flows will be put in S 1 , the set of flows to be directly transmitted from the BS. In Lines 14-24, flows whose QoS

Algorithm 1: The Transmission Method Selection Algorithm
Input: S all , S1 = ∅, S2 = ∅, QoS requirements of flows, locations of the BS, UAV, eavesdropper and MRs Output: S1, S2 1 Calculate q a l 1 , q a l 2 and q a l 2 for each flow; 2 Remove D = {f |q a l 1 < q f and min{q a l 2 , q a l 2 } < q f then 9 if C l 1 E can satisfy (14) then 10 S1 = S1 ∪ f ; requirements can be satisfied by both transmission methods will be divided into three categories according to the secrecy capacity condition and transmission efficiency. If the secrecy capacity condition cannot be satisfied, it will be put in the set of flows relayed by the UAV, as in Lines 15-16. Finally, the remaining flows select their transmission methods by comparing the required transmission time of different choices as in  For the complexity of the transmission method selection algorithm, the number of the iterations of the outer for loop in Line 3 is |S all |, where |S all | in the worst case is O(F ). Hence, the computational complexity of the algorithm is O(F ).

B. The UAV-assisted Scheduling Algorithm
Next we present a transmission scheduling algorithm. This scheduling algorithm is developed on the basis of the above transmission method selection algorithm.
We select the appropriate links for transmission in each TS based on the principle of maximizing the number of transmitted flows. For ease of exposition, we first introduce the simultaneous transmission requirements of links and the priority of link scheduling. Regarding the contention among links, we consider the three cases as follows.
1) Due to half-duplex transmissions, the link from the BS to the UAV and the link from the UAV to an MR cannot be scheduled concurrently. 2) If the simultaneous transmissions of two links will cause at least one of them to fail to satisfy the QoS requirement, there is a conflict between the two links. (14), the links transmitted from the BS in the f 1 frequency band that do not meet the safe capacity threshold will not be scheduled simultaneously with other links. A flow f relayed by the UAV will require two links l 2 and l 2 to be scheduled, i.e., l 2 from the BS to UAV and l 2 from the UAV to MR. We define the set of links scheduled in the f 1 band as S f1 , and the set of links scheduled in the f 2 band as S f2 . Link l 2 should be scheduled before l 2 . Thus we first schedule link l 2 ∈ S f1 , and then link l 2 is generated in S f2 to wait for being scheduled.

3) As stated in
Then we consider the transmit order of the links in S f1 or S f2 . A parameter is defined to prioritize the scheduling order of the links, which is the inverse of a link's number of required TSs in a frame to satisfy its QoS requirement. For link i which is generated from flow f , the priority value is given by .
In fact, the less time the transmissions consume, the more flows can be completed in a fixed time interval. Prioritizing the flows that need fewer TSs can transmit more flows in a frame. Therefore, we calculate the priority values of links in S f1 and S f2 as (17), and then transmit the links in the descending order in the scheduling process. Since there are always new links joining S f2 , the scheduling order of the links in the set needs to be frequently updated.
We define the set of the links which are to be transmitted from the BS to UAV as S BU , and a parameter N f is used to indicate the number of completed flows. The QoS-aware scheduling algorithm is summarized in Algorithm 2. First, the input and output of the algorithm are specified. Then, we configure sets S f1 and S BU in Lines 1-2. In Lines 7-14, we find the links in S f1 that can be scheduled simultaneously with other selected transmitted links. In Lines 15-22, the simultaneous scheduled links in S f2 are determined. In Lines 23-40, we obtain the remaining throughput demands of all the scheduled links after this time slot, and examine whether there are flows which have finished transmission in this TS.
The outer for loop of the scheduling algorithm has O(M ) iterations. The for loop in Line 8 has |S f1 | iterations, and |S f1 | in the worst case is O(F ). The for loop in Line 18 has |S f2 | iterations with the worst case value O(F ). Moreover, the for loop in Line 23 has |SS| iterations, and its worst case value is also O(F ). The for loops in Line 8, Line 18 and Line 23 are parallel. Therefore, the complexity of the QoS-based scheduling algorithm is O(M F ). Such low complexity of the algorithm makes it suitable for practical implementation.

VI. PERFORMANCE EVALUATION
In this section, we evaluate the performance of the UAVassisted scheduling algorithm. The flow transmissions of the Algorithm 2: The UAV-assisted Scheduling Algorithm Input: S1, S2, SBU = ∅, S f 1 = S1, S f 2 = ∅, N f = 0, Calculate the priority values of the links in S f 1 ; 5 Sort the links in S f 1 in increasing order by priority value; scheme involve two bands: f 1 in the 28GHz band and f 2 in the 60GHz band. We also compare the performance of the proposed scheme with several existing baseline schemes.

A. Simulation Setup
We consider the single cell scenario of the mmWave HSR system. There are 24 MRs which are evenly distributed on top of the train. The flows which need to be scheduled are all transmitted from the BS. Then the flows are generated by randomly selecting the receivers among all the MRs. The train has a total of eight cars with a total length of 200m. Three MRs are deployed in each car. One eavesdropper is randomly placed on top of the train. The BS and UAV can reach a communication range of hundreds of meters, and all the links in the system are transmitted within the allowable communication ranges of their transceivers. The number of flows which need to be transmitted between the BS and MRs is smaller than the number of MRs.
Since the MRs move at the high speed with the train, we need to update theirs location information periodically. We collect and update the location information of MRs every 2000 time slots in the process. During this period of time, the change distances of MRs do not exceed 3m, which does not affect the data transmissions.
Besides, each carriage of the train has 50-100 seats, then each MR has to deal with the data traffic requirements of 10-20 passengers at most. The flow of each MR request may be a combination of the traffic flows of multiple passengers. Thus, the QoS requirement values of scheduled flows fluctuates in a wide range. We assume that the QoS requirement of each flow is uniformly distributed between 10Mbps and 500Mbps. Other parameters are presented in Table I [14], [35], [39]. For the path loss model, the specific expression is given in (2) and the parameter settings are from [39], which are summarized in Table II. For the performance evaluation study, the following performance metrics are used.
• Number of completed flows: the number of scheduled flows with their QoS requirements satisfied. If the QoS requirement cannot be met, the flow that has been scheduled will not be counted as a completed flow. • System throughput: the total throughput of the entire network system. This metric is the sum of the average throughputs of all the flows carried in the network. • Total amount of time slots: the total number of time slots used to transmit all the scheduled flows. The actual value of this metric must be smaller than the total number of TSs in the frame. For comparison purpose, we implement the state-of-theart QoS-aware Concurrent Scheme [17] and Maximum QoS-aware Independent Set (MQIS) based scheduling algorithm [19] as baseline schemes.
• QoS-aware Concurrent Scheme [17]: this is a concurrent scheduling scheme aiming to satisfy the QoS requirements of flows and to maximize the number of flows. If a flow cannot meet the current scheduling conditions, the scheme cannot relay the flow and has to give up scheduling it. • MQIS [19]: this is the scheduling algorithm based on the concept of maximum QoS-aware independent set proposed in [19]. The algorithm divides flows into multiple independent sets. Only after all the flows in one set are scheduled transmitted, the flows in another set can begin to be scheduled.

B. Comparison with Existing Schemes
In Fig. 2, we plot the number of completed flows under different numbers of requested flows. The horizontal distance between the UAV and BS is set to 150m, and the number of time slots is set to 8,000. With the increased number of requested flows, we can see that the curves for the three schemes are all rising. The more requested flows in the HSR network, the more flows can complete their scheduling. For the proposed UAV-assisted scheduling scheme, some flows which cannot be directly transmitted from the BS can choose to be scheduled by UAV relaying. Therefore, the proposed scheme achieves a superior performance with respect to the number of completed flows compared with the other two schemes. When the number of flows is 18, the proposed scheme outperforms the QoS-aware concurrent scheme with a gain of 35.0% and MQIS with a gain of 32.4%.
In Fig. 3, we plot the system throughput under different numbers of requested flows. The horizontal distance between the UAV and BS and the number of time slots are the same as in Fig. 2. From the figure, we can see that the system throughput of the three schemes are all increasing with the increased number of requested flows. For each flow, our proposed scheme can decide whether to use UAV relaying according to the principle of transmission rate maximization. The proposed scheme achieves the largest system throughput among the three scheme. It can also relay some flows which cannot be directly transmitted from the BS. The QoS-aware scheme and MQIS have similar performance on the system throughput. When the number of requested flows is 18, the  proposed UAV-assisted scheduling scheme outperforms the QoS-aware concurrent scheme with a gain of 64.6% and MQIS with a gain of 61.2%.
In Fig. 4, we plot the system throughput under different numbers of requested flows. The horizontal distance between the UAV and BS and the number of time slots remain the same as previous simulations. From this figure, we can see that the total number of slots of the three schemes are all increasing as more flows are requested. Although the proposed scheme can schedule the most flows among the three schemes, as shown in Fig. 2, it can be seen from Fig. 4 that the total numbers of time slots needed by the three schemes are very similar. This proves that the proposed UAV-assisted scheme has a superior performance on transmission efficiency compared with the QoS-aware concurrent scheme and MQIS.
In Fig. 5, we plot the number of completed flows under different numbers of time slots in each frame. The horizontal distance between the UAV and BS is set to 150 m, and the  number of requested flows is set to 18. When the number of time slots begin to increases, the curves of the three schemes are all rising. When the number of time slots increases to a certain value, the numbers of completed flows of three schemes are all becoming steady. Therefore, the number of time slots does not need to be increased to a very large value; an appropriate value of time slot number in each scheme can ensure a satisfactory performance.
In Fig. 6, we plot the system throughput under different numbers of time slots. The horizontal distance between UAV and BS and the number of requested flows are the same as that in Fig. 5. From the figure, we can see similar trends for the three schemes as in Fig. 5. With the increased number of time slots, we can see that the curves for the three schemes are all rising. The more time slots, the more system throughput can achieve during scheduling. The three curves start to stabilize when the number of slots is increased over 8,000. Hence, the number of TSs is set to 8,000 in the other simulations.  In Fig. 7, we plot the number of completed flows under different horizontal distances between UAV and BS, to demonstrate the impact of the UAV location on performance. The number of requested flows is set to 18, and the number of time slots is set to 8,000. With the increased horizontal UAV-BS distance, the number of completed flows increases first and then declines. Thus a suitable location of the UAV has a significant impact on the system performance. When UAV-BS distance is 150 m, the number of completed flows reaches the maximum value. Thus, the horizontal distance between the UAV and BS is set to 150 m in all the other simulations.

VII. CONCLUSIONS
In this paper, we considered the problem of scheduling flows with diverse QoS requirements in the scenario of mmWave HSR communications. To maximize the number of flows while satisfying their QoS requirements, we proposed the UAVassisted scheduling scheme to deploy a UAV relay to assist the scheduling of flows. Our simulation study showed that the proposed UAV-assisted scheme outperformed two baseline schemes on the number of completed flows, achievable system throughput, and transmission efficiency.