A post-disaster emergency network resource management method for aerial base station assistance

Abstract

This method proposes a device-to-infrastructure (D2I) and device-to-device (D2D) collaboration framework for post-disaster emergency networks assisted by aerial base stations (ABS), and studies the resource management problem on this basis. First, by considering the rate requirements and reliability of the D2I and D2D collaboration frameworks to optimize power control and spectrum allocation, a multi-objective optimization problem is formulated to characterize the trade-off between two key performance indicators, namely: a) minimization of total power consumption (primary); b) maximization of sum rate (secondary); then the explicit expression of the optimal power control strategy is theoretically derived; then the spectrum allocation problem is transformed into a bipartite graph maximum matching problem, and the optimal spectrum allocation strategy is obtained using the Hungarian algorithm. The resource management algorithm designed by the present invention reduces the total power consumption by 54.2%, effectively reduces power consumption and maximizes sum rate, achieves energy saving and performance enhancement, and achieves an ideal balance between the two performance indicators.

Description

Technical Field

[0001] This method is mainly applied to post-disaster emergency networks assisted by air base stations (ABS). A device-to-infrastructure (D2I) and device-to-device (D2D) collaboration framework is proposed, which involves the field of resource management in communication networks. Background Art

[0002] Unforeseen natural disasters will cause conventional communication infrastructure to be unavailable or communication links to be severely interrupted. In order to restore connectivity in the affected areas, high requirements are placed on the deployment of aerial base stations to meet emergency communication needs. In post-disaster emergency communication scenarios, ABS can hover above the target area to provide temporary access services to cellular users within a specific horizontal distance.

[0003] Considering the limited energy and resources of ABS, the prior art 1 proposes a post-disaster rescue task offloading scheme using the ABS-assisted device-to-infrastructure method. In order to further improve the channel capacity and energy efficiency, the prior art 2 analyzes the average achievable rate of the ABS-assisted post-disaster emergency network (PDENET) and provides a large number of numerical results to explain the performance improvement brought by ABS. However, the access of a large number of cellular users will lead to communication congestion and a large number of spectrum resource requirements, which poses a serious challenge to the PDENET based on D2IBA.

[0004] Faced with this challenge, D2D communication can be considered as a promising approach to improve the spectrum efficiency of ABS-assisted PDENETs, ​​and the user quality of experience (QoE) can enable devices to communicate directly without infrastructure. Prior art studies have studied the power control problem of D2D communication to improve energy efficiency and reduce the average transmit power; maximize the sum rate by maximizing the number of visited D2D links; and improve spectrum efficiency by optimizing the allocation of user pairing and mode selection. However, considering several practical factors such as the rate requirements of D2I links and the reliability of D2D links, it is still unclear how to optimize limited resources to improve channel capacity and energy efficiency in ABS-assisted PDENETs.

[0005] In order to achieve energy saving and performance enhancement, a D2I and D2D collaborative framework for ABS-assisted emergency communications is proposed in this approach. High-capacity D2I links transmit emergency-related bandwidth-intensive services, and high-reliability D2D links ensure information sharing between users. Resource management issues are studied in the considered framework by optimizing power control and spectrum allocation. Summary of the invention

[0006] Aiming at the problems existing in the existing technologies, this method proposes a D2I and D2D collaboration framework for resource management in post-disaster emergency networks assisted by ABS.

[0007] The method is implemented as follows: first, a multi-objective optimization problem is proposed by optimizing power control and spectrum allocation, and the original decomposition method is used to decompose the problem; then, an explicit expression of the optimal power control strategy is derived theoretically; then the spectrum allocation problem is transformed into a bipartite graph maximum matching problem, and the problem is solved by the Hungarian algorithm.

[0008] Furthermore, the D2I and D2D collaboration framework for resource management in the post-disaster emergency network based on ABS assistance includes the following steps:

[0009] The first step is to build an air-ground collaborative emergency communication network model consisting of an aerial base station and C cellular users. In the network under consideration, C cellular users require high-capacity device-to-infrastructure communication, and there are D pairs of cellular users for local high-reliability data exchange in the form of device-to-device;

[0010] The second step is to build an air-to-ground channel model based on the wireless link loss;

[0011] The third step is to model the resource management problem of air-ground collaborative IoT based on the basic rate requirements of the D2I link and the reliability of the D2D link.

[0012] The fourth step is to develop an optimal power control strategy based on the rate and maximization scheme of the D2I link;

[0013] The fifth step is to use the Hungarian algorithm to obtain the best spectrum allocation strategy based on the optimal power control strategy.

[0014] Furthermore, an air-ground collaborative emergency communication network model consisting of an air base station and C cellular users is constructed. Specifically, it includes:

[0015] Step (1.1), in the network under consideration, C cellular users require high-capacity device-to-infrastructure communication, and there are D pairs of cellular users who exchange data locally in a highly reliable D2D manner, defined as Define the symbol d t and d r To distinguish the dth The sending and receiving ends of a user pair;

[0016] Step (1.2), in order to improve spectrum utilization, the orthogonally allocated D2I uplink spectrum can be reused by D2D communication. This method defines the spectrum reuse scheme as If the user The spectrum of the D2I link is reused by the dth D2D user pair, rc,d =1, otherwise r c,d =0, that is, r c,d ∈{0,1}. In addition, the transmission power sets for D2I and D2D communications are defined as and

[0017] Step (1.3), the signal-to-noise ratio (SINR) γ of user c during D2I communication c It is expressed as:

[0018]

[0019] where h c,B is the small-scale fading between user c and ABS, η c,B is the large-scale fading between user c and ABS, is d t Small-scale fading between ABS, is d t Large-scale fading between ABS, σ 2 is the noise power;

[0020] Step (1.4), SINRγ of the dth D2D user pair d It is expressed as:

[0021]

[0022] in, is d t and d r The small-scale fading between is d t and d r The large-scale fading between For users c and d r The small-scale fading between For users c and d r The large-scale fading between is the basic SINR requirement for the D2D link.

[0023] Furthermore, the second step of constructing an air-to-ground channel model based on the wireless link loss specifically includes:

[0024] Step (2.1), for air-to-ground (A2G) channels, the wireless link loss (dB) usually consists of two parts, namely the line-of-sight (LoS) component and non-light-of-sight (NLoS) components They are:

[0025]

[0026] and

[0027]

[0028] Among them, d c,B is the straight-line distance from user c to ABS, f c is the carrier frequency, v is the speed of light, η 1 and η 2 are the average shadow fading of LoS component and NLoS component respectively.

[0029] In step (2.2), according to equations (S.3) and (S.4), the probability P(LoS) of the LoS component and the probability P(NLoS) of the NLoS component appearing in the Internet of Things assisted by the aerial base station are respectively expressed as:

[0030]

[0031] and

[0032]

[0033] Among them, d c,B is the straight-line distance from user c to ABS, f c is the carrier frequency, v is the speed of light, η 1 and η 2 are the average shadow fading of LoS component and NLoS component respectively.

[0034] Step (2.3), according to equations (S.1)-(S.6), the path loss PL from user c to ABS is c,B It is expressed as:

[0035]

[0036] Furthermore, the third step is to model the air-ground collaborative IoT resource management problem based on the basic rate requirement of the D2I link and the reliability of the D2D link, and specifically includes:

[0037] Step (3.1), this method studies the resource management problem of air-ground collaborative IoT based on the basic rate requirement of D2I link and the reliability of D2D link. and power distribution Aims to maximize the rate and sum of D2I and D2D links while minimizing device power consumption;

[0038] Step (3.2), the multi-objective optimization problem is modeled as:

[0039]

[0040] Among them, B 0 is the channel bandwidth, is the minimum capacity requirement of the D2I link, and They are the maximum transmit powers for D2I and D2D communications respectively.

[0041] In P1, constraints (8c) and (8d) guarantee the basic rate requirements and reliability of the D2I link and D2D link respectively; constraints (8e) and (8f) define the transmission power ranges of D2I and D2D communications respectively; constraints (8g) and (8h) indicate that the spectrum of each D2I link can only be reused to one D2D user pair, and each D2D user pair can only access the spectrum of one D2I link. This assumption can effectively reduce the cross-layer interference between D2D user pairs and cellular users in the reuse mode and reduce the complexity of the algorithm.

[0042] Furthermore, the fourth step, based on the rate and maximization scheme of the D2I link, formulates an optimal power control strategy, which specifically includes:

[0043] Step (4.1) decouples the modeled problem and focuses on each cellular user and D2D communication pair to minimize device power consumption. The power allocation problem is expressed as:

[0044]

[0045] Step (4.2), to satisfy constraint (8c), is expressed as:

[0046]

[0047] Step (4.3), to satisfy constraint (8d), is expressed as:

[0048]

[0049] Step (4.4), according to equations (S.10) and (S.11), the power allocation problem is rewritten as:

[0050]

[0051] P2.2 is a linear programming problem. According to the feasible domain of P2.2, the analytical solution of P2.2 can be obtained, that is, the optimal transmission power (P c D2I ) * and They are:

[0052]

[0053] and

[0054]

[0055] Furthermore, the fifth step is based on the optimal power control strategy and uses the Hungarian algorithm to obtain the optimal spectrum allocation strategy, which specifically includes:

[0056] Step (5.1): Substitute the optimal power allocation (P c D2I ) * and into the c-th D2I link and the d-th D2D link, so as to obtain the maximum capacity

[0057]

[0058] Step (5.2): The spectrum reuse problem can be simplified as follows:

[0059]

[0060] In P3, since each (P c D2I ) * and both satisfy and Therefore, constraints (8c) and (8d) can be omitted.

[0061] Step (5.3): P3 can be regarded as a bipartite graph matching problem, and the Hungarian algorithm is used to obtain the maximum matching (i.e., ).

[0062] To sum up, the advantages and positive effects of this method are as follows: In response to the problems existing in the prior art, this method proposes a D2I and D2D cooperation framework in the ABS-assisted PDNET, and on this basis, studies the resource management problem. The designed resource management algorithm can jointly obtain the optimal power control strategy for the D2I and D2D cooperation frameworks. The complexity of spectrum allocation is such that it can find the global optimal solution for P1, calculate the optimal power allocation for each D2I user c and D2D user pair d, and on this basis, it can find the best spectrum reuse scheme among all possible D2D user pairs.

[0063] Compared with the existing mechanisms, this method can effectively reduce the total power consumption, enable high-capacity D2I links to transmit emergency-related bandwidth-intensive services, and high-reliability D2D links to ensure information sharing among users, achieving energy conservation and enhancing communication performance.

[0064] This method compares the performance of the PDENET designed for the aerial base station with other algorithms (power control algorithm, maximum access link algorithm and the proposed algorithm applied to the ground base station). The comparison chart is as follows: Figure 4 The relationship between D2I and D2D link sum rates and SINR requirements of D2D links is as follows: Figure 5 . BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flowchart of a D2I and D2D collaboration framework based on resource management in a post-disaster emergency network assisted by ABS provided by an embodiment of the method.

[0066] Figure 2 This is a D2D enhanced drone communication network model consisting of an aerial base station and C cellular users provided by the embodiment of the method.

[0067] Figure 3 This is the matching model for the spectrum allocation problem provided by the embodiment of the method.

[0068] Figure 4 It is a performance comparison of the designed algorithm provided in the embodiment of the method applied to ABS, GBS and other algorithms (power control algorithm, maximum access link algorithm).

[0069] Figure 5 This is the relationship between the D2I and D2D links and rates and the SINR requirements of the D2D links provided by the embodiment of the method. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the method more clear, the method is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described here are only used to explain the method and are not used to limit the method.

[0071] In view of the problems existing in the prior art, the present invention provides a post-disaster emergency network resource management method assisted by an aerial base station. The present method is described in detail below in conjunction with the accompanying drawings.

[0072] The D2I and D2D collaboration framework for resource management in a post-disaster emergency network assisted by an ABS provided by the present method embodiment includes the following steps:

[0073] S101: Construct an air-ground collaborative emergency communication network model consisting of an air base station and C cellular users.

[0074] S102: constructing an air-to-ground channel model based on wireless link loss;

[0075] S103: Modeling of air-ground collaborative IoT resource management problem based on the basic rate requirement of D2I link and the reliability of D2D link;

[0076] S104: formulating an optimal power control strategy based on the rate and maximization scheme of the D2I link;

[0077] S105: Based on the optimal power control strategy, the Hungarian algorithm is used to obtain the optimal spectrum allocation strategy.

[0078] The technical solution of the present method is further described below in conjunction with specific embodiments.

[0079] The D2I and D2D collaboration framework for resource management in a post-disaster emergency network assisted by an ABS provided by the present method embodiment includes the following steps:

[0080] The first step is to build an air-ground collaborative emergency communication network model consisting of an air base station and C cellular users. Specifically, it includes:

[0081] Step (1.1), in the network under consideration, C cellular users require high-capacity device-to-infrastructure (D2I) communications, and there are D pairs of cellular users who exchange data locally in a highly reliable device-to-device (D2D) format, defined as Define the symbol d t and d r To distinguish the dth The sending and receiving ends of a user pair;

[0082] Step (1.2), in order to improve spectrum utilization, the orthogonally allocated D2I uplink spectrum can be reused by D2D communication. This method defines the spectrum reuse scheme as If the user The spectrum of the D2I link is reused by the dth D2D user pair, r c,d =1, otherwise r c,d =0, that is, r c,d ∈{0,1}. In addition, the transmission power sets for D2I and D2D communications are defined as and

[0083] Step (1.3), the signal-to-noise ratio (SINR) γ of user c during D2I communication c It is expressed as:

[0084]

[0085] where h c,B is the small-scale fading between user c and ABS, η c,Bis the large-scale fading between user c and ABS, is d t Small-scale fading between ABS, is d t Large-scale fading between ABS, σ 2 is the noise power;

[0086] Step (1.4), SINRγ of the dth D2D user pair d It is expressed as:

[0087]

[0088] in, is d t and d r The small-scale fading between is d t and d r The large-scale fading between For users c and d r The small-scale fading between For users c and d r The large-scale fading between is the basic SINR requirement for the D2D link.

[0089] The second step, which constructs an air-to-ground channel model based on the wireless link loss, specifically includes:

[0090] Step (2.1), for air-to-ground (A2G) channels, the wireless link loss (dB) usually consists of two parts, namely the line-of-sight (LoS) component and non-light-of-sight (NLoS) components They are:

[0091]

[0092] and

[0093]

[0094] Among them, d c,B is the straight-line distance from user c to ABS, f c is the carrier frequency, v is the speed of light, η 1 and η 2 are the average shadow fading of LoS component and NLoS component respectively.

[0095] In step (2.2), according to equations (S.3) and (S.4), the probability P(LoS) of the LoS component and the probability P(NLoS) of the NLoS component appearing in the Internet of Things assisted by the aerial base station are respectively expressed as:

[0096]

[0097] and

[0098]

[0099] Among them, d c,B is the straight-line distance from user c to ABS, f c is the carrier frequency, v is the speed of light, η 1 and η 2 are the average shadow fading of LoS component and NLoS component respectively.

[0100] Step (2.3), according to equations (S.1)-(S.6), the path loss PL from user c to ABS is c,B It is expressed as:

[0101]

[0102] The third step is to model the air-ground collaborative IoT resource management problem based on the basic rate requirements of the D2I link and the reliability of the D2D link, and specifically includes:

[0103] Step (3.1), this method studies the resource management problem of air-ground collaborative IoT based on the basic rate requirement of D2I link and the reliability of D2D link. and power distribution Aims to maximize the rate and sum of D2I and D2D links while minimizing device power consumption;

[0104] Step (3.2), the multi-objective optimization problem is modeled as:

[0105]

[0106] Among them, B 0 is the channel bandwidth, is the minimum capacity requirement of the D2I link, and They are the maximum transmit powers for D2I and D2D communications respectively.

[0107] In P1, constraints (8c) and (8d) guarantee the basic rate requirements and reliability of the D2I link and D2D link respectively; constraints (8e) and (8f) define the transmission power ranges of D2I and D2D communications respectively; constraints (8g) and (8h) indicate that the spectrum of each D2I link can only be reused to one D2D user pair, and each D2D user pair can only access the spectrum of one D2I link. This assumption can effectively reduce the cross-layer interference between D2D user pairs and cellular users in the reuse mode and reduce the complexity of the algorithm.

[0108] The fourth step is to formulate an optimal power control strategy based on the rate and maximization scheme of the D2I link, which specifically includes:

[0109] Step (4.1) decouples the modeled problem and focuses on each cellular user and D2D communication pair to minimize device power consumption. The power allocation problem is expressed as:

[0110]

[0111] Step (4.2), to satisfy constraint (8c), is expressed as:

[0112]

[0113] Step (4.3), to satisfy constraint (8d), is expressed as:

[0114]

[0115] Step (4.4), according to equations (S.10) and (S.11), the power allocation problem is rewritten as:

[0116]

[0117] P2.2 is a linear programming problem. According to the feasible domain of P2.2, the analytical solution of P2.2 can be obtained, that is, the optimal transmission power (P c D2I ) * and They are:

[0118]

[0119] and

[0120]

[0121] Step 5: The step 5 uses the Hungarian algorithm to obtain the best spectrum allocation strategy based on the optimal power control strategy, and specifically includes:

[0122] Step (5.1), the optimal power allocation (Pc D2I ) * and Substitute the cth D2I link and the dth D2D link to obtain the maximum capacity

[0123]

[0124] Step (5.2), the spectrum reuse problem can be simplified to:

[0125]

[0126] In P3, since each (P c D2I ) * and All meet and Therefore constraints (8c) and (8d) can be omitted.

[0127] Step (5.3), P3 can be regarded as a bipartite graph matching problem, and the maximum matching (i.e. ).

[0128] The technical effect of this method is described in detail below in conjunction with simulation.

[0129] This experiment simulates the D2I and D2D collaboration framework for resource management in ABS-assisted post-disaster emergency networks, and evaluates the performance of the designed resource management algorithm to verify the superiority of this method. The specific simulation parameters are as follows: the transmission power is 23dBm, the carrier frequency is 2.1GHz, the number of cellular users is 100, the number of D2D user pairs is 60, the basic SINR value is set to 50dB, the basic rate requirement is 6.5bps / Hz, the channel bandwidth is 180kHz, the ABS coverage radius is 800m, the average shadow fading LoS is 1.0dB, the NLoS is 20dB, and the noise power is -150dBm / Hz. The results are the average values ​​after 5000 simulations.

[0130] The performance of this method is compared with the power control algorithm and the maximum access link algorithm and the PDENET assisted by the ground base station (GBS). The relationship between the D2I and D2D link sum rate and the SINR requirement of the D2D link is given, such as Figure 4 , Figure 5 shown.

[0131] In summary, the embodiment of the method aims at the problems existing in the prior art, proposes a D2I and D2D collaboration framework in ABS-assisted PDNET, and studies the resource management problem on this basis. Firstly, by considering the rate requirements and reliability of the D2I and D2D collaboration framework, a multi-objective optimization problem is formulated, the purpose is to optimize the balance between total power consumption (primary) and total rate (secondary); then the original decomposition method is used to deal with the problem; then the explicit expression of the optimal power control strategy is derived. On this basis, the optimal spectrum allocation strategy is obtained using the Hungarian algorithm. Compared with the existing mechanism, this method can effectively reduce the total power consumption, enable high-capacity D2I links to transmit emergency-related bandwidth-intensive services, and high-reliability D2D links to ensure information sharing between users, so as to achieve a better balance between power consumption and total rate so as to be effectively applied to emergency communication scenarios, achieve energy saving and enhance communication performance.

[0132] The above description is only a preferred embodiment of the present method and is not intended to limit the present method. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present method should be included in the protection scope of the present method.

Claims

1. D2I and D2D Collaborative Framework for Resource Management in Post-Disaster Emergency Networks Assisted by Aerial Base Stations The following steps are involved: The first step is to build an air-ground collaborative emergency communication network model consisting of an aerial base station and C cellular users; The second step is to build an air-to-ground channel model based on the wireless link loss; The third step is to model the resource management problem of air-ground collaborative IoT based on the basic rate requirements of the D2I link and the reliability of the D2D link. The fourth step is to develop an optimal power control strategy based on the rate and maximization scheme of the D2I link; Step 5: Based on the optimal power control strategy, the Hungarian algorithm is used to obtain the optimal spectrum allocation strategy; The first step is to construct an air-ground collaborative emergency communication network model consisting of an air base station and C cellular users, which specifically includes: Step (1.1), assume that there are D pairs of cellular users that exchange data in a local, highly reliable manner in a D2D manner, defined as D = {1, 2, ..., D}, and define the symbol d t and d r To distinguish the dth one, The transmitter and receiver of a D2D user pair; Step (1.2), this method defines the spectrum reuse scheme as If user c, The spectrum of the D2I link is reused by the dth D2D user pair, r c,d =1, otherwise r c,d =0, that is, r c,d ∈{0,1}, and the transmit power sets for D2I and D2D communications are defined as and Step (1.3), the signal-to-noise ratio (SINR) γ of user c during D2I communication c It is expressed as: where h c,B is the small-scale fading between user c and ABS, η c,B is the large-scale fading between user c and ABS, is d t Small-scale fading between ABS, is d t Large-scale fading between ABS, σ 2 is the noise power; Step (1.4), the dth D2D user pair It is expressed as: in, is d t and d r The small-scale fading between is d t and d r The large-scale fading between For users c and d r The small-scale fading between For users c and d r The large-scale fading between The basic SINR requirement for the D2D link; The second step of constructing an air-to-ground channel model based on wireless link loss specifically includes: Step (2.1), for air-to-ground (A2G) channels, the wireless link loss usually consists of two parts, namely the line-of-sight (LoS) component and non-light-of-sight (NLoS) components They are: and Among them, d c,B is the straight-line distance from user c to ABS, f v is the carrier frequency, v is the speed of light, η 1 and η 2 are the average shadow fading of LoS component and NLoS component respectively; In step (2.2), according to equations (S.3) and (S.4), the probability P(LoS) of the LoS component and the probability P(NLoS) of the NLoS component appearing in the Internet of Things assisted by the aerial base station are respectively expressed as: and Step (2.3), according to equations (S.1)-(S.6), the path loss PL from user c to ABS is c,B It is expressed as: The third step is to model the air-ground collaborative IoT resource management problem based on the basic rate requirements of the D2I link and the reliability of the D2D link, which specifically includes: Step (3.1) is to jointly optimize the spectrum reuse R and power allocation (P D2I , P D2D ), minimize device power consumption and maximize the sum of the rates of D2I and D2D links; Step (3.2), the multi-objective optimization problem is modeled as: Among them, B 0 is the channel bandwidth, is the minimum capacity requirement of the D2I link, and are the maximum transmit powers for D2I and D2D communications respectively; The fourth step is to formulate an optimal power control strategy based on the rate and maximization scheme of the D2I link, which specifically includes: Step (4.1), in order to minimize the device power consumption, the power allocation problem is expressed as: Step (4.2), to satisfy constraint (8c), is expressed as: Step (4.3), to satisfy constraint (8d), is expressed as: Step (4.4), according to equations (S.10) and (S.11), the power allocation problem is rewritten as: P2.2 is a linear programming problem. According to the feasible domain of P2.2, the analytical solution of P2.2 can be obtained, that is, the optimal transmission power for D2I and D2D communication. and They are: and The fifth step is based on the optimal power control strategy and adopts the Hungarian algorithm to obtain the optimal spectrum allocation strategy, which specifically includes: Step (5.1), allocate the optimal power and Substitute the cth D2I link and the dth D2D link to obtain the maximum capacity Step (5.2), the spectrum reuse problem can be simplified to: In step (5.3), P3 can obtain the maximum matching by using the Hungarian algorithm, namely (R) * .

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