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Quantum-Assisted Routing Optimization for Self-Organizing Networks

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4 Author(s)
Alanis, D. ; Sch. of Electron. & Comput. Sci., Univ. of Southampton, Southampton, UK ; Botsinis, P. ; Soon Xin Ng ; Hanzo, L.

Self-organizing networks act autonomously for the sake of achieving the best possible performance. The attainable routing depends on a delicate balance of diverse and often conflicting quality-of-service requirements. Finding the optimal solution typically becomes an nonolynomial-hard problem, as the network size increases in terms of the number of nodes. Moreover, the employment of user-defined utility functions for the aggregation of the different objective functions often leads to suboptimal solutions. On the other hand, Pareto optimality is capable of amalgamating the different design objectives by providing an element of elitism. Although there is a plethora of bioinspired algorithms that attempt to address this optimization problem, they often fail to generate all the points constituting the optimal Pareto front. As a remedy, we propose an optimal multiobjective quantum-assisted algorithm, namely the nondominated quantum optimization algorithm (NDQO), which evaluates the legitimate routes using the concept of Pareto optimality at a reduced complexity. We then compare the performance of the NDQO algorithm to the state-of-the-art evolutionary algorithms, demonstrating that the NDQO algorithm achieves a near-optimal performance. Furthermore, we analytically derive the upper and lower bounds of the NDQO algorithmic complexity, which is of the order of O(N) and O(N√(N)) in the best and worst case scenario, respectively. This corresponds to a substantial complexity reduction of the NDQO from the order of O(N2) imposed by the brute-force method.

NDQO simulation for a 6-node SON for 300 frames: The SON is assumed to be covering a ($100times 100$) m square block area, where the Source Node (SN) and the Destination Node (DN) are located at the opposite corners of this square block and they are stationary. The Mobile Relay Nodes are denoted in the video by the red triangle ($triangle$) marker and are referred to as $mathrm{R}I$, where we have $I in{1, 2, 3, 4}$. Their initial locations are random, obeying a uniform distribution within the square block area. A new random destination is reached after 100 frames. Each of the $mathrm{R}I$s and the DN experience Gaussian interference at each random location and the interference power is produced by the complex Gaussian process $CN(-90, 10)$ in dBm. Their levels are noted above the marker of each $mathrm{R}I$ and at the bottom legend located at the right hand side of the video. A linear interpolation process is used for simulating the evolution of the levels of interference between the random locations for each $mathrm{R}I$ and the DN. The NDQO algorithm is employed for each frame for the sake of identifying the Pareto Optimal routes in terms of the Bit Error Ratio (BER) and the total power dissipation of each route. The routes are marked in the respective graph with arrows of distinct colors per route. Moreover, QPSK transmissions over uncorrelated Rayleigh channel are assumed and the loss model has a path loss exponent of $a = 3$. A list of the optimal routes is shown in the top legend located at the right hand side of the video clip.

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Access, IEEE  (Volume:2 )
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