Network overload is one of the key challenges in wireless LANs (WLANs). This goal is typically achieved when the load of access points (APs) is balanced. Recent studies on operational WLANs, shown that AP load is often uneven distribution. To rectify such overload, several load balancing schemes have been proposed. These methods are commonly require proprietary software or hardware at the user side for controlling the user-AP association. In this paper we present a new load balancing method by controlling the size of WLAN cells (i.e., APís coverage range), which is conceptually similar to cell breathing in cellular networks.
This method does not require any modification to the users neither the IEEE 802.11 standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We develop a set of polynomial time algorithms that find the optimal beacon power settings which minimize the load of the most congested AP. We also consider the problem of network-wide min-max load balancing. Simulation results show that the performance of the proposed method is comparable with or superior to the best existing association-based method.
Cell breathing has been studied mostly in the context of CDMA cellular networks. The coverage and capacity of a CDMA cell are inversely related with each other .The increase of the number of active users in a cell causes the increase of the total interference sensed at the base station. Therefore, in congested cells, users need to transmit with higher power to maintain a certain signal-to-interference ratio at the receiving base station. As the users in a congested cell increase their transmission power, they also increase their interference to the neighboring cells since all cells use the same frequency in CDMA networks. As a result, the overall network capacity may decrease. Furthermore, since the maximal transmission power of the users is bounded, the users who are far from the base station may experience poor services. To overcome these problems, the cell breathing approach was proposed. In essence, they reduce the size of congested cells.
we address the problem of minimizing the load of the congested APs. Let us call the AP with the maximal load as congested AP and its load as congestion load. We designed two polynomial time algorithms that find optimal solutions, one for the complete knowledge model and the other for the limited knowledge model. These results are intriguing, because similar load balancing problems are known to be strong NP-hard. It is particularly interesting that a polynomial time optimal algorithm exists for the limited knowledge model. Second, we address the problem of min-max load balancing. This is a strong NP-hard problem. In , it is proved that there exists no algorithm that guarantees any coordinate wise approximation ratio, and the approximation ratio of any prefix-sum approximation algorithm is at least nlogn , where n is the number of APs. In this paper, we solve a variant of this min-max problem, termed min-max priority load balancing, whose optimal solution can be calculated in polynomial time for both knowledge models.Here, the AP load is defined as an ordered pair of the aggregated load contributions of its associated users and a unique AP priority.
- Client Model
- Server Model
- Network Model
- Algorithmic Challenges
- Cell Breathing Approach
- Bottleneck Method
- Congestion Load Minimization
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