User–Base-Station Association in HetSNets: Complexity and Efficient Algorithms

Abstract

Abstract-This work considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigated, which are maximizing the set of associated users to the SBSs (the unweighted problem) and maximizing the set of weighted associated users to the SBSs (the weighted problem), under signal-to-interference-plus-noise ratio constraints. Both problems are formulated as linear integer programs. The weighted problem is known to be NP-hard and, in this paper, the unweighted problem is proved to be NP-hard as well. Therefore, this paper develops two heuristic polynomial-time algorithms to solve both problems. The computational complexity of the proposed algorithms is evaluated and is shown to be far more efficient than the complexity of the optimal brute-force (BF) algorithm. Moreover, the paper benchmarks the performance of the proposed algorithms against the BF algorithm, the branchand-bound CPLEX-based algorithm and state-of-the-art algorithms, through numerical simulations. The results demonstrate the close-to-optimal performance of the proposed algorithms. They also show that the weighted problem can be solved to provide solutions that are fair between users or to balance the load among SBSs.

Latency-Aware Dynamic Resource Allocation Scheme for Multi-Tier 5G Network: A Network Slicing-Multitenancy Scenario

“…In the multi-tier heterogeneous network, the base station-slice user association is formulated as an integer programming problem [47], [48]. It is given as:…”

Multiobjective self‐optimization of the cellular architecture for green 5G networks

“…It is obvious that there is no deterministic algorithm able to explore the whole search space to find a solution in a polynomial time. This is normal since MOSON is a CSO problem, which is well known as NP‐hard problem . To tackle MOSON, we have used the GA to find good suboptimal solutions.…”

“…It is important to note that all objectives are normalized (0 ≤ Obj ≤ 1) for robust normalized fitness calculation. Our multiobjective self-optimization framework addresses many combinatorial NP-hard problems: capacity, energy efficiency, dynamic network planning (sectorization and design tuning), and load balancing, 9,[11][12][13][14][15][16]22,23 and these cannot be resolved in deterministic time. Their realistic projection and implementation constraints in a live network increase the complexity even further, especially when the traffic and network layout vary in temporal and spatial planes.…”

“…This is normal since MOSON is a CSO problem, which is well known as NP-hard problem. 9,[11][12][13][14][15]22,23 To tackle MOSON, we have used the GA to find good suboptimal solutions.…”

“…• We have used 3D sectorization, 3GPP 3D channel model, and 3D UE dropping that we consider mandatory to zoom in the performance of UEs located in the elevation dimension such as high-rise buildings. Most previous research studies on sectorization have used 2D channel model, passive antenna systems, and UE dropping at 1.5 m. [3][4][5][6][8][9][10][11][12][13][14][15] • Unlike probabilistic traffic estimation, we have used a real history of hourly traffic and key performance indicators (KPIs) of a live LTE-Advanced network (3GPP Release 12) to forecast the nearby future traffic trends. It is a precise way to show the traffic fluctuation during the day and to exploit peak-off time and busy hours to trigger CSO.…”