Mutual interference among users can abruptly increase othercell interference and cause overload situation in coexisting WCDMA and HSDPA systems. Traffic overloading can degrade the performance of the systems. This letter proposes a new dynamic downlink load control (DDLC) algorithm to reduce performance degradation due to overload in the coexistence of WCDMA and HSDPA systems. With the proposed algorithm, the downlink load is controlled according to load states classified by two load-control thresholds, and traffic overloading is alleviated by dynamically adjusting the CQI values reported by users, based on the downlink load as well as channel variations. The proposed algorithm is simulated and results show that the DDLC scheme improves the performance of both WCDMA and HSDPA systems in terms of outage probability, total system throughput, and radio resource utilization.

In this paper, we propose distributed medium access control (MAC) protocols based on an adaptive sensing period adjustment scheme for low-cost multiple secondary users in interweave-type cognitive radio (CR) networks. The proposed MAC protocols adjust the sensing period of each secondary user based on both primary sensing and secondary data channels in distributed manner. Then, the secondary user with the shortest sensing period accesses the medium using request-to-send (RTS) and clear-to-send (CTS) message exchange. Three components affect the length of each user's sensing period: sensing channel quality from the primary system, data channel quality to the secondary receiver, and collision probability among multiple secondary transmitters. We propose two sensing period adjustment (SPA) schemes to efficiently improve achievable rate considering the three components, which are logarithmic SPA (LSPA) and exponential SPA (ESPA). We evaluate the performance of the proposed schemes in terms of the achievable rate and other factors affecting it, such as collision probability, false alarm probability, and average sensing period.

With rapid increase of the number of applications as well as the sizes of data, multi-query processing on the MapReduce framework has gained much attention. Meanwhile, there have been much interest in skyline query processing due to its power of multi-criteria decision making and analysis. Recently, there have been attempts to optimize multi-query processing in MapReduce. However, they are not appropriate to process multiple skyline queries efficiently and they also require modifications of the Hadoop internals. In this paper, we propose an efficient method for processing multi-skyline queries with MapReduce without any modification of the Hadoop internals. Through various experiments, we show that our approach outperforms previous studies by orders of magnitude.