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Inspired by the increasingly mature vehicle-to-everything (V2X) communication technology, we propose a multihop V2X downlink transmission system to improve users’ quality of experience (QoE) in hot spots. Specifically, we develop a cross-layer resource allocation algorithm to optimize the long-term system performance while guaranteeing the stability of data queues. Lyapunov optimization is employed to transform the long-term optimization problem into a series of instantaneous subproblems, which involves the joint optimization of rate control, power allocation, and mobile relay selection at each time slot. On one hand, the optimization of rate control is decoupled and carried out independently. On the other hand, a low-complexity pricing-based stable matching algorithm is proposed to solve the joint power allocation and mobile relay selection problem. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance and simultaneously guarantee queue stability.

With the explosive growth of mobile Internet and the rapid expansion of massive data traffic services, mobile communication systems put forward higher requirements for network capacity, especially in hot spots with high user density. In this case, users’ quality of experience (QoE) will drop sharply due to the limited network resources and the possibility of blocking and packet loss. Consequently, the reliability of wireless communication system cannot be guaranteed [

To address this challenge, many researchers have proposed deploying more microcells, picocells, or relays, expecting to increase the capacity and coverage of the network [

On the other hand, the development of vehicular networks becomes more and more mature [

However, establishing this V2X-based multihop relay system faces the following major challenges. First, how to control the stability of data queues considering the dynamic data arrival and departure remains an open issue. It involves not only the optimization of rate control at the network layer but also the optimization of power allocation and relay selection in the physical layer. Therefore, the traditional resource allocation of physical layer scheme will no longer be applicable, and a cross-layer resource optimization scheme is needed. Second, the joint optimization of rate control, power allocation, and relay selection is formulated as a mixed integer nonlinear optimization problem, since both binary and continuous optimization variables are involved. How to solve this nonlinear optimization problem effectively is another challenge. Last but not least, conventional resource allocation schemes mainly focus on short-term optimization and ignore long-term system performance and constraints. At each round of optimization, the resources are allocated in a greedy way without considering the future, which results in long-term performance degradation. However, it is infeasible to derive the optimal long-term optimization solution due to the absence of the future knowledge. Therefore, how to achieve low-complexity online resource allocation with guaranteed performance requires further study.

To tackle these challenges, we propose a multihop V2X communication system to improve the network capacity and realize the long-term reliable transmission for users densely distributed in hot spots. It mainly involves the rate control in the network layer, as well as the mobile relay selection and power allocation in the physical layer. The objective is to optimize the long-term users’ QoE performance under the constraints of long-term power consumption and queue stability.

The main contributions of this paper are summarized as follows:

A multihop downlink communication system based on V2X is established. In this system, vehicles as mobile relays play the roles of transmitting traffic from BS to users. In such system, vehicles are not only extensions of base station (BS) transmission distance, but also methods for BS to expand coverage and improve system throughput.

A cross-layer long-term optimization is established for the multihop V2X communication system. Such long-term optimization cannot be resolved by existing approaches. Motivated by this, the recently-developed Lyapunov optimization approach is leveraged. With its assistance, the long-term optimization problem is transformed into a gradual optimization problem. First, the long-term optimization is formulated to obtain the optimal long-term QoE under the constraints of long-term power consumption and delay. Second, the constraints of long-term power consumption and delay are transformed into virtual queues stability problem. Finally, the long-term performance optimization is decomposed into a series of online subproblems involving trading off queue backlog and utility.

An online algorithm based on Lyapunov optimization is proposed. The above mentioned online subproblem is divided into the rate control problem in network layer and the resource allocation problem in physical layer. Faced with this, the rate control problem is solved by the convex optimization easily. However, the physical layer resource allocation problem, as a mixed integer nonlinear optimization problem, is difficult to be solved. Further, we prove that the resource allocation problem is a convex problem, given the mobile relay selection results. In the sense, the price-based matching is proposed to deal with the mobile relay selection problem. This online algorithm only needs to know the current channel and queue information, without foreknowledge of global information. These works successfully reduce the signalling overheads and computational complexity and conform to the time causality.

The rest of this paper is organized as follows. The related work is introduced in Section

The concept of V2X communication which helps to support variety of traffic-efficient applications has drawn great attention in both industrial and academic fields recently [

Several works have focused on the service queue stability to improve users’ QoE [

Resource allocation problems in V2X communication have been studied in [

Figure

System model. (a) V2X communications system. (b) Queue diagram of the system.

The system operates in a time-slotted manner with timeslot index

It is worth noting that

As we know, the higher data rate brings more enjoyable experience to users. However, marginal utility theory sets forth a viewpoint: When other inputs remain unchanged, increasing a certain input continuously will result in a gradual decrease in the new income. On this basis, we define QoE

To improve the transmission capacity of the downlink, AF protocol is applied to vehicles with single antenna, which are called mobile relays in this paper.

As illustrated in Figure

AF collaboration system model.

The mobile relay is assumed to be operated in half-duplex mode; i.e., it cannot transmit and receive simultaneously. The transmission from BS and the relay node are done over orthogonal channels in time domain [

When users receive signals with maximum ratio combining (MRC) [

To be simple, this paper just considers the power allocation of BS to the queues, and the transmission power of relay is fixed. The queues’ transmission power is constrained by two practical limitations. One is inspired with the energy saving of BS, which is to limit the long-term averaged power consumption and it is set as

The aim of this paper is to maximize the long-term QoE of this system, which is shown as

Due to the queuing model (

Lyapunov optimization is efficient in designing stable control algorithms. It has been extended to treat network stability and performance optimization simultaneously. In this section, the online algorithm based on Lyapunov optimization toward (

The long-term power constraint

Similarly, the delay-constrained virtual queue

Subsequently, let

Shown as (

The RC subproblem is illustrated as

In addition, the RSPA subproblem is shown as

The Hessian matrix of

Lemmas

Given the relay selection results, the Hessian matrix of

Theorem

RSPA is a mixed integer nonlinear programming problem. And its constraints

The matching problem can be described as a triple

Matching at timeslot

In order to minimize the RSPA problem, we can define that the preference as

Obtaining the preference matrix

Then the price-based matching algorithm is introduced as Algorithm

(

Calculate

Initialize

proposal from queues is defined as

For any mobile relay

(

If

preference according to Equation (

Each queue

If any mobile relay

with the mobile relay which sends the initial proposal. Otherwise, add

If

proposal.

Each mobile relay

Every queue which has proposed to

Remove the vehicles which receive only proposal from

The comprehensive theoretical analyses of matching on convergence, stability, optimality, and complexity have been proved in [

(

(

(

(

(

(

(

(

(

(

(

(

(

In Algorithm

The aim of this work is to solve the long-term optimization (

Bounds of the optimal utility and the average congestion satisfy

Actually, Algorithm

Algorithm

At the second step, BS will select the optimal mobile relay according to the current channel information which could be obtained through the help of mobile relays. In the process of price-based matching, the complexity for each queue to acquire the preferences of all the vehicles and the complexity for sorting the obtained preferences are

At the third step, under the mobile relay selection results, BS will allocate the limited power to users according to RSPA. Theorem

Moreover, we can find the system will be stabilized after several timeslots, which will be illustrated in the simulation results. And the V2X communication system could support higher performance to users.

In this section, simulation results are presented. The stability and tradeoff between utility and queue backlog are analyzed. What is more, we compare long-term performance of two communication system, including the V2X multihop communication system in this paper and the traditional direct link communication system. For convenience, the work of long-term optimization with AF (LTAF) under V2X multihop communication system is called LTAF, and similarly the work of long-term optimization with direct link (LTDL) is called LTDL. The simulation parameters have been illustrated in Table

Simulation parameters.

Parameters | Value |
---|---|

| |

| |

| |

| 0 watts |

| 2.5 watts |

| 8 watts |

| 50 Mbps |

| 4 |

| 0.1 |

As expected, the queues

Queue backlogs versus time.

Delay-constrained virtual queues versus time.

Power-constrained virtual queue versus time and the box plot of

Utility versus time.

Arrival rate versus time.

Transmission capacities versus time.

Actual delay. (a) Actual delay versus time. (b) The bound and average actual delay of different queues. The error bars represent the variance of measurements for 200 timeslots.

At the same time, the stability of queues reveals three issues:

The robustness of queue congestion is guaranteed. In other words, the queue backlog is limited in a controllable range. So the system will not crash due to heavy traffic owing to the rate control of BS.

The latency requirements of users are satisfied. Moreover, the actual delay is always lower than the upper bound of delay, shown as Figure

The limited power resource is used effectively, and the long-term power constraint is accomplished. If we assume

We can find that

In addition, we can find

Firstly, we will explain the baseline. Baseline is the results of individually optimizing the utility and transmission capacity, respectively. LTAF is the result of our algorithm. Figure

The tradeoff between utility and queue backlog versus

For LTAF, we can observe that the utility is in inverse proportion to

In this part, the main work is to compare the performance of queue backlog

Figure

Queue backlog of LTDL versus time and error bar for comparison. The error bars represent the variance of measurements for 200 timeslots.

In Figure

Delay-constrained virtual queue backlog of LTDL versus time and error bar for comparison. The error bars represent the variance of measurements for 200 timeslots.

In Figures

Figure

Power-constrained virtual queue backlog of LTDL versus time and error bar for comparison. The error bars represent the variance of measurements for 200 timeslots.

In this paper, a cross-layer resource allocation schemes for V2X communication system was investigated. First, the downlink transmission for BS with the help of vehicles as mobile relays was proposed. These mobile relays play roles in extending the coverage distance of the BS and increasing the capacity. Second, to establish the cross-layer resource allocation problem, the queue model was applied to couple the arrival rate in the network layer with the transmission capacity in the physical layer. Third, Lyapunov optimization was proposed to transfer the long-term optimization problem to series of rate control and mobile relay selection-power allocation problems at each timeslot. And the tradeoff between queue backlog and utility was evaluated. Fourth, expecting low computational complexity, the mobile relay selection was optimized with matching. Then simulation results verified that LTAF could reach the stability as expected; LTAF could obtain the optimal utility with the worst queue backlog; and LTAF not only could reduce the burden on BSs but also could satisfy the diversity requirements of user business; i.e., LTAF could accomplish the latency demands of different queues at each time slot but the LTDL could not.

In future work, we will focus on rental-based relay selection problem for V2X communication system. In this paper, the vehicle is a free boost for BS. In fact, the vehicle, as a private property, is difficult to open for users freely.

To prove Lemma

Then, the second-order partial derivative

Combining the results of (

At first, we assume that, under such V2X multihop communication system, the expectation of

After a certain number of iterations as Algorithm

This paper focuses on minimizing the drift-minus-function. Plugging (

Similarly, (

The data used to support the findings of this study are included within the article. The data is shown in Table

The authors declare that they have no conflicts of interest.

Professor Zhenyu Zhou provided help during the research and preparation of the manuscript. And this work is supported by the Fundamental Research Funds for the Central Universities (2018QN003).