Figures (6)  Tables (2)

    • Figure 1. 

      Multi-relay network with an RF-EH scenario.

    • Figure 2. 

      Improved HTC protocol.

    • Figure 3. 

      Different resource allocation algorithms based on SE.

    • Figure 4. 

      The SE convergence of the proposed algorithm.

    • Figure 5. 

      Comparison of EE performance of different resource allocation algorithms.

    • Figure 6. 

      The energy efficiency convergence of the proposed algorithm.

    • 1. Initialize.
      2. Set the iteration termination times $ N_{outer}^{max} $, iteration termination accuracy $ {\varpi }_{outer} $, and the initial iteration value $ {\eta }_{0}=0 $ and $ n=0 $.
      3. Loop body:
      4. Update iteration index $ n=n+1 $.
      5. Solving the Optimization Problem $ F({\eta }_{n-1})=\underset{\mathbf{\Phi },\mathbf{P}}{\max }\left[{R}_{total}(\mathbf{\Phi },\boldsymbol{P})-{\eta }_{n-1}{\mathbf{P}}_{total}(\mathbf{\Phi },\mathbf{P})\right] $, gain $ \left\{{\mathbf{\Phi }}^{*},{\mathbf{P}}^{*}\right\} $.
      6. Calculate the energy efficiency under the current iterative index using $ \left\{{\mathbf{\Phi }}^{*},{\mathbf{P}}^{*}\right\} $, $ {\eta }_{n}={R}_{total}({\mathbf{\Phi }}^{*},{\boldsymbol{P}}^{\text{*}})/{\mathbf{P}}_{total}({\mathbf{\Phi }}^{*},{\mathbf{P}}^{*}) $.
      7. End Condition: $ \left| {\eta }_{n}-{\eta }_{n-1}\right| < {\varpi }_{outer} $ or $ n > N_{outer}^{max} $.
      8. Output optimal solution $ \left\{{\eta }^{*},{\mathbf{\Phi }}^{*},{\mathbf{P}}^{*}\right\} $.

      Table 1. 

      Energy efficiency optimization iterative algorithm.

    • 1. Initialize: Set the maximum number of outer loop iterations Mmax, the dual variables $ \mu $ and $ \lambda $, set the outer loop iteration factor m = 0.
      2. Outer Loop:
      3. Initialize the maximum number of inner loop iterations Nmax and $ {\tau _I} $, and set the inner loop iteration factor n = 0;
      4. According to Eq. (37) and ε(i, j), and using the golden section method, calculate A(i, j);
      5. Inner Loop:
      6. Calculate ϕ(i, j) according to Eq. (46) and Hungarian algorithm;
      7. Update $ {\tau _I} $ according to Eqs (44), (45);
      8. Through n = n + 1, update the inner loop iteration factor n;
      9. End condition of inner loop: Inner loop convergence or n = Nmax.
      10. According to Eq. (46), $ {\tau _I} $ and A(i,j) calculate P(i,j);
      11. Use the sub-gradient method to update $ \mu $ and $ \lambda $, $ {\tau _I} $,P(i,j) and A(i,j);
      12. Through m = m + 1, update the outer loop iteration factor m;
      13. End condition of outer loop: Outer loop convergence or m = Mmax.
      14. Calculate $ P_i^{S1} $, $ P_{i,j}^{S2} $ and $ P_{i,j,k}^R $ according to Eq. (18) and Eqs (40), (41).
      15. Calculate Pi,j using Eqs (31)−(33).
      16. Output $ \Phi $ and P.

      Table 2. 

      Energy-efficient inner resource allocation algorithm, solving the optimization problem F(ηn−1).