Figures (5)  Tables (3)

    • Figure 1. 

      EAU-ISAC scenario.

    • Figure 2. 

      EAU-ISAC protocol.

    • Figure 3. 

      Perceived spectral efficiency with N = 4 and M = 4.

    • Figure 4. 

      Perceived spectral efficiency with N = 4 and M = 12.

    • Figure 5. 

      Relationship between perceived spectrum efficiency and number of antenna.

    • 1. Initialization: Set the iteration termination number $ N_P^{max} $, iteration termination accuracy $ {\varepsilon _P} $, and iteration initial values $W_n^c,\forall n$ and $\omega _m^s,\forall m$.
      2. Loop body:
      3. Update iteration index i = i + 1;
      4. Solve the optimization problem $ F({q_i}) = \mathop {\max }\limits_{W_n^c,W_m^s} A $ and obtain $ \left\{ {W_n^{c*},W_m^{s*}} \right\} $;
      5. Perform EVD decomposition on $ W_n^{c*} $ and $ W_m^{s*} $, and calculate $w_n^c,\forall n$ and $w_m^s,\forall m$ under the current iteration index;
      6. End condition: $ \left| {F({q_i}) - F({q_{i - 1}})} \right| < {\varepsilon _P} $ or $ i > N_P^{max} $.
      7. Output the optimal solution $ \left\{ {{q^*},W_n^{c*},W_m^{s*}} \right\} $.

      Table 1. 

      The optimization algorithm for the beamforming vector under a fixed UAV position.

    • 1. Initialization: Set the iteration termination number $ N_u^{max} $, iteration termination accuracy $ {\varepsilon _u} $, and iteration initial values u.
      2. Loop body:
      3. Update iteration index i = i + 1;
      4. Solve the optimization problem F(qi) = $ \mathop {\max }\limits_u A $ and obtain u*;
      5. Analyze the position information of u* and calculate the UAV position
      (x, y, h0) under the current iteration index;
      6. End condition: | F(qi) − F(qi−1) | < ${\varepsilon _u} $ or i > $N_u^{max} $.
      7. Output the optimal solution {q*, u*}.

      Table 2. 

      UAV position optimization algorithm under a fixed beamforming vector.

    • 1. Initialization: Set the termination times $ N_{outer}^{max} $ and $ N_{inner}^{max} $ for the outer loop and inner loop, as well as the termination precision $ {\varepsilon _{outer}} $ and $ {\varepsilon _{inner}} $
      2. External circulation body :
      3. Set the initial values q0 = 0 and n = 0 for the outer loop;
      4. Using binary method to solve the optimal energy harvesting time slot allocation factor τ;
      5. Internal circulation body 1:
      6. Set the iteration termination number $ N_P^{max} $, iteration termination accuracy $ {\varepsilon _P} $, and iteration initial values $W_n^c,\forall n$ and $\omega _m^s,\forall m$;
      7. Run Algorithm 1 to solve the optimization problem and run F(qi) = $ \mathop {\max }\limits_{W_n^c,W_m^s} A $.
      8. Output the optimal solution $ \left\{ {{q^*},W_n^{c*},W_m^{s*}} \right\} $;
      9. End condition of inner loop 1: | F(qi − F(qi−1) | < $ {\varepsilon _P} $ or i > $ N_P^{max} $
      10. Internal circulation body 2:
      11. Set the iteration termination number $ N_u^{max} $, iteration termination accuracy $ {\varepsilon _u} $, and iteration initial value u;
      12. Run Algorithm 2 to solve the optimization problem F(qi) = $ \mathop {\max }\limits_u A $;
      13. Output the optimal solution {q*, u*};
      14. End condition for inner loop 2: | F(qi) − F(qi−1) | < $ {\varepsilon _u} $ or i > $ N_u^{max} $.
      15. Calculate F(q) = $ \mathop {\max }\limits_{\tau ,u,\omega } A $ and update q;
      16. End condition of outer loop: | qn − qn−1) | < $ {\varepsilon _{outer}} $ or n > $ N_{outer}^{max} $.
      17. Output the optimal solution {q*, τ*, u*, $ \omega ^* $} and obtain the joint resource allocation scheme for the energy harvesting assisted UAV sensing integration system.

      Table 3. 

      UAV position optimization algorithm under a fixed beamforming vector.