Satisfying the huge demand for high bandwidth in 5G networks is in part achieved by vertical densification of the network infrastructure with so-called small-cell base stations. As a direct consequence, the performance of 5G networks intrinsically suffers from: 1) a huge inter-cell interference due to the network density and 2) a large energy waste that results from the high redundancy of lightly loaded, always-on, and small-cell base stations. In this paper, we use a game theoretic approach to design a distributed energy efficient bandwidth sharing mechanism for small-cell networks. To circumvent the combinatorial nature of searching in the huge strategy space, we invoke a reinforcement learning approach to intelligently guide the search toward good solutions. More precisely, our solution relies on dynamically learning good strategies for the user-equipment association and orthogonal frequency division multiple access (OFDMA) scheduling to strike a balance between energy efficiency and high bandwidth utilization. Our model takes into account inter-cell interference and the diversity of user quality of service requirements.

In this paper, we describe a software defined networking (SDN) approach to traffic engineering in inter-data center communication; then present$\mathrm{MCTEQ}$, a constrained utility-optimization formulation of the joint-bandwidth allocation problem for multiple classes of traffic in inter-data center communication.$\mathrm{MCTEQ}$handles priorities between traffic classes in a soft manner and explicitly considers the delay requirement of$\mathrm{Interactive}$flows.$\mathrm{MCTEQ}$being NP-hard, we construct a new approximation to be able to lean on the mature and efficient linear programming solvers to obtain fast and accurate approximations. We demonstrate via numerical experiments with two realistic inter-data center network topologies that$\mathrm{MCTEQ}$achieves considerably more network utilization than the best known solutions from the literature, while running at least twice faster. We also show via ns-2 packet level simulation that with$\mathrm{MCTEQ}$traffic engineering, end-to-end packet delay requirements of$\mathrm{Interactive}$flows are indeed guaranteed for in-profile traffic, which is not the case for alternative approaches.

The increasing demand for high bandwidth in cellular wireless networks led to a dramatic increase in the number of deployed LTE femtocell base stations (FBS) in recent years. While the energy consumed by a single FBS is relatively negligible, the collective energy footprint of all FBSs deployed by an operator turns out to be huge. Energy-efficient protocols are needed thus to balance the trade-off between energy saving and bandwidth utilization. The key component of the desired protocol to prevent both energy and bandwidth waste is interference mitigation, which, most previous work has failed to properly consider. To this end, in this paper, we manipulate user equipment (UE) association and OFDMA scheduling with a combination of interference mitigation to enable an energy efficient QoS-constrained medium access for femtocell networks. Recognizing the NP-hardness of the problem, we propose two algorithms with guaranteed convergence. Extensive simulations show that our algorithms outperform alternatives in multiple metrics such as utility, power consumption, and convergence speed.

To improve their profits, it is crucial for cloud service providers to fulfil their tenants target QoS requirements in an energy efficient way. The services provided in many modern public clouds have graduated to include more sophisticated and flexible virtual clusters, encompassing not only the resource requirement of the virtual machines (VMs) but also the virtual topology between VMs. In this paper, we study the energy efficiency of virtual cluster embedding in data centers. We carefully develop a mathematical model, aiming to minimize the energy consumption of a data center, and given the NP- hardness of the problem, we propose an approximate algorithm MinE-VCE to solve the problem efficiently. We show via numerical experiments how MinE-VCE consistently outperforms other alternative algorithms.

Powering an individual LTE femtocell base station, or Home eNodeB (HeNB) in the LTE jargon, requires relatively very little energy. It is only when HeNBs are deployed massively, as has happened in the past few years, that energy efficiency becomes an important issue. With large numbers of co-located HeNBs and the increased inter-cell interference resource utilization becomes highly inefficient resulting in a high unnecessary energy consumption. To tackle this problem, coordination techniques could be invoked by the network operator to consolidate the offered workload to the network on as few HeNBs as possible and power down idle ones. Recognizing, however, that such techniques usually impair user-perceived quality of service (QoS), especially with bursty traffic, more sophisticated methods need to be investigated to also consider QoS. Despite the volume of prior work, the key issue -- viz. simultaneously reducing energy waste, increasing bandwidth utilization while guaranteeing user-perceived QoS -- has not been properly considered so far. In this paper, we model the trade-off between energy efficiency and QoS preservation by manipulating user equipment (UE) association and OFDMA scheduling in controlled networks of HeNBs. The problem being NP-hard, we propose two distributed learning algorithms, within a potential game-based framework, to obtain good and fast solutions to the problem. We demonstrate via numerical results the effectiveness of the proposed algorithms in achieving better performance in terms of utility, power, energy efficiency, convergence speed, and complexity compared to other alternatives.

With the increased density of LTE femtocell base station (FBSs) deployments, mutual interference between FBSs becomes a bottleneck to network performance. Inter-femtocell coordination has been invoked in the past to address this problem, however it is not practically possible as LTE FBSs do not possess the X2 interface to enable them to communicate with each other. In this paper, we opt for a fully distributed approach and introduce a self-learning MAC protocol for FBSs to allocate resources based on historical user feedback. In our approach, each FBS independently learns about the asymmetric contention level on its links, and transfers resources from highly-contended links to low-contention ones to avoid wasting resources. The learning process is modelled as a cluster-structural regret-based game and simulation results show that our algorithm outperforms alternative algorithms from the literature on a wide range of metrics.

In dense indoor multi-cell wireless networks, such as WLANs and OFDMA-based femtocell networks (e.g., LTE, WiMAX), the wireless bandwidth waste has become more dramatic due to the large interference and contention occasioned by uncoordinated channel access methods such as CSMA. Coordinating resource allocation in the network can be a good compromise solution to this problem. However, in general, resource allocation in wireless networks is a complex multi-dimensional problem that involves four tasks: client association to select a base station to associate with, antenna beam selection when directional antennas are used, link scheduling to ensure conflict freedom, and power adaptation to reduce mutual interference. In this paper we study the joint optimization of the above four components, and propose a unified conflict-free scheduling algorithm that solves the joint problem with two alternative objectives: (1) power-utility-maximization and (2) fair-throughput-maximization. Our scheduling algorithm can be directly implemented in OFDMA-based femtocell networks. In addition, to enable realistic deployment in WLANs, we design the so-called TD-CSMA, a coordinated access protocol that is compatible with the legacy IEEE 802.11 MAC protocol. With extensive simulations in ns-2 we notably show that our model outperforms some benchmark algorithms on a wide range of metrics.

In this paper we study the problem of client association, airtime sharing and contention resolution in a multi-cell WLAN to achieve fair download bandwidth sharing. We first model the problem as a utility maximization problem, then, because of the integral nature of association, the problem being NP-hard, we decompose it into two sub-problems: the client association problem on one hand, and the joint airtime sharing and contention resolution problem on the other. We show that, for each fixed association pattern, the corresponding joint airtime sharing and contention resolution problem can be solved in polynomial time. To solve the client association problem, we propose the so-called Swap localized algorithm. The Swap procedure iteratively swaps clients between pairs of neighbour access points (APs) if the swap leads to an improvement in the global utility. Simulation results show that, the proposed algorithm is significantly better than RSSI-based heuristic, and quickly leads to a near-optimal solution.