It is increasingly common in digital environments to use A/B tests to compare the performance of recommendation algorithms. However, such experiments often violate the stable unit treatment value assumption (SUTVA), particularly SUTVA's ''no hidden treatments'' assumption, due to the shared data between algorithms being compared. This results in a novel form of bias, which we term ''symbiosis bias,'' where the performance of each algorithm is influenced by the training data generated by its competitor. In this paper, we investigate three experimental designs--cluster-randomized, data-diverted, and user-corpus co-diverted experiments--aimed at mitigating symbiosis bias. We present a theoretical model of symbiosis bias and simulate the impact of each design in dynamic recommendation environments. Our results show that while each design reduces symbiosis bias to some extent, they also introduce new challenges, such as reduced training data in data-diverted experiments. We further validate the existence of symbiosis bias using data from a large-scale A/B test conducted on a global recommender system, demonstrating that symbiosis bias affects treatment effect estimates in the field. Our findings provide actionable insights for researchers and practitioners seeking to design experiments that accurately capture algorithmic performance without bias in treatment effect estimates introduced by shared data.

DDoS attacks have emerged as one of the most serious network security threats in 5G, IoT, multi-cloud, and other emerging technology scenarios. The bandwidth of DDoS attacks is increasing in the new scenario, but the current network structure and security devices are inflexible. We propose a DDoS protection method based on SDN multi-dimensional scheduling method and DDoS scrubbing policy, which not only plans the scheduling path, but also blocks and redirects different kinds of attack traffic that used dynamic residual bandwidth of links, the number of flow entries in OpenFlow switches, and scheduling path length. To flexibly protect against DDoS attacks, this method combines scheduling and protection means. The experimental results indicate that the scheduling is effective. The scheduling, path produced by this method outperforms ECMP and KSP approaches in throughput, packet loss rate, and jitter, and it can block L3/L4 attack traffic and redirect L7 attack traffic.

This paper presents a novel downlink resource allocation scheme for OFDMA-based next generation wireless networks subject to inter-cell interference (ICI). The scheme consists of radio resource and power allocations, which are implemented separately. Low-complexity heuristic algorithms are first proposed to achieve the radio resource allocation, where graph-based framework and fine physical resource block (PRB) assignment are performed to mitigate major ICI and hence improve the network performance. Given the solution of radio resource allocation, a novel distributed power allocation is then performed to optimize the performance of cell-edge users under the condition that desirable performance for cell-center users must be maintained. The power optimization is formulated as an iterative barrier-constrained water-filling problem and solved by using the Lagrange method. Simulation results indicate that our proposed scheme can achieve significantly balanced performance improvement between cell-edge and cell-center users in multi-cell networks compared with other schemes, and therefore realize the goal of future wireless networks in terms of providing high performance to anyone from anywhere.

In this paper we propose a novel resource allocation scheme to achieve a balanced performance improvement for all users in a LTE network subject to inter-cell interference. In the proposed scheme the resource allocation process is implemented in two steps. In the first step interference coordination and scheduling are first conducted in a global manner to prevent cell-edge users from mutual interference. In the second step, optimal power allocation is conducted to maximize performance of cell-edge users while maintaining high performance of cell-center users. The optimal power allocation problem is solved using the Lagrange decomposition method. Simulation results show that our proposed scheme can significantly improve performance for all users in a multi-cell network and achieve a better performance balance between cell-edge and cell-center users.

The decode-and-forward relay technique has been introduced in next generation wireless networks (such as LTE) to extend coverage and improve performance, although it may generate additional inter-cell interference. Soft frequency reuse (SFR) has been proposed as the most promising frequency planning strategy to mitigate inter-cell interference in LTE systems. In this paper we propose an effective combination of relay networks and SFR, with a dedicated relay topology and the SFR-based resource allocation scheme. In the proposed relay network, each relay station (RS) can jointly serve cell-edge users from adjacent cells to increase efficiency. The proposed resource allocation scheme, on the other hand, is able to achieve throughput improvement for both cell-edge users and cell-center users by minimizing the interference impact in the system. The benefit of relay networks with SFR-based resource allocation in terms of 30% overall performance improvement over conventional non-relay networks can be realized as demonstrated by the simulation results.

This paper proposes a novel load distribution aware soft frequency reuse (LDA-SFR) scheme for inter-cell interference mitigation and performance optimization in next generation wireless networks. Our proposed scheme aims to provide a solution to effectively achieve inter-cell interference mitigation while maintaining high spectrum efficiency to all users in the cell. The proposed scheme consists of two novel algorithms: edge bandwidth reuse and centre bandwidth compensation. Using the edge bandwidth reuse algorithm, cell-edge users can take advantage of uneven traffic load and user distributions within each cell to expand their resource allocations. The center bandwidth compensation algorithm, on the other hand, provides a protection mechanism for cell-center users to avoid exhaustive edge bandwidth extension. Applying LDA-SFR to an LTE network and comparing its performance against that of existing soft frequency reuse (SFR) and adaptive soft frequency reuse (ASFR) schemes indicates that LDA-SFR is superior as it achieves fairness between cell-edge users and cell-center users in terms of average throughput improvement.

In this paper we consider the inter-network interference problem in Wireless Body Area Networks (WBANs). We propose a distributed inter-network interference aware power control algorithm motivated by game theory. A power control game is formulated considering both interference between nearby networks and energy efficiency of WBANs. We derive a distributed power control algorithm called ProActive Power Update (PAPU), which can efficiently find the Nash Equilibrium representing the best tradeoff between energy and network utility. A realistic power control procedure is proposed assuming limited cooperation between WBANs. We compare our algorithm with the ADP algorithm where users are punished for interfering with others and we show that our solution can utilize energy much more efficiently by only sacrificing a small amount of network utility. In addition, we show that by adjusting the energy price, PAPU provides a methodology for application scenarios where WBANs have different energy constraints and quality of service requirements.