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To support the ever-expanding demands for multifarious vehicular services with a limited spectrum, vehicle-to-everything (V2X) networks, which underlay cellular networks, have drawn extensive attention. The underlaid mode suffers from catastrophic cochannel interference caused by spectrum sharing between cellular users and V2X users, thus reducing the sum rate of the system. The existing solutions for mitigating cochannel interference focus mainly on the physical domain without considering the influence of the social domain. This may greatly limit the sum-rate enhancement potential of the utilized system. To address this issue, a social mobility-aware V2X underlaying a cellular network is studied in this chapter. By jointly optimizing the vehicle pairing situation and resources (i.e., the spectrum and power), a sum-rate maximization problem is formulated for V2X-underlaid cellular networks while satisfying the diverse quality of service (QoS) requirements of both cellular users and vehicular users. The formulated problem is proven to be a nondeterministic polynomial-time (NP)-hard problem and is difficult to directly solve. As an alternative, we propose a joint vehicle pairing, spectrum assignment, and power control algorithm in Sect. 3.3. In this section, the original problem is decomposed into two disjoint subproblems, i.e., (1) a joint vehicle pairing and spectrum assignment subproblem and (2) a power control subproblem. To address the first subproblem, we propose a heuristic social mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-Meyer-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, by solving the second subproblem, a closed-form power solution is obtained via a three-dimensional geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum rate and outperforms the baseline algorithms by approximately 24–53% in Sect. 3.4.

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Social Mobility-Aware Communication Resource Management

  • 章节

  • 发表日期：

    2025年

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  • Haixia Zhang,
  • Dongyang Li,
  • Tong Xue

摘要

To support the ever-expanding demands for multifarious vehicular services with a limited spectrum, vehicle-to-everything (V2X) networks, which underlay cellular networks, have drawn extensive attention. The underlaid mode suffers from catastrophic cochannel interference caused by spectrum sharing between cellular users and V2X users, thus reducing the sum rate of the system. The existing solutions for mitigating cochannel interference focus mainly on the physical domain without considering the influence of the social domain. This may greatly limit the sum-rate enhancement potential of the utilized system. To address this issue, a social mobility-aware V2X underlaying a cellular network is studied in this chapter. By jointly optimizing the vehicle pairing situation and resources (i.e., the spectrum and power), a sum-rate maximization problem is formulated for V2X-underlaid cellular networks while satisfying the diverse quality of service (QoS) requirements of both cellular users and vehicular users. The formulated problem is proven to be a nondeterministic polynomial-time (NP)-hard problem and is difficult to directly solve. As an alternative, we propose a joint vehicle pairing, spectrum assignment, and power control algorithm in Sect. 3.3. In this section, the original problem is decomposed into two disjoint subproblems, i.e., (1) a joint vehicle pairing and spectrum assignment subproblem and (2) a power control subproblem. To address the first subproblem, we propose a heuristic social mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-Meyer-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, by solving the second subproblem, a closed-form power solution is obtained via a three-dimensional geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum rate and outperforms the baseline algorithms by approximately 24–53% in Sect. 3.4.