Kyuho Son

Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks

Energy-efficiency, one of the major design goals in wireless cellular networks, has received much attention lately, due to increased awareness of environmental and economic issues for network operators. In this paper, we develop a theoretical framework for BS energy saving that encompasses dynamic BS operation and the related problem of user association together. Specifically, we formulate a total cost minimization that allows for a flexible tradeoff between flow-level performance and energy consumption. For the user association problem, we propose an optimal energy-efficient user association policy and further present a distributed implementation with provable convergence. For the BS operation problem (i.e., BS switching on/off), which is a challenging combinatorial problem, we propose simple greedy-on and greedy-off algorithms that are inspired by the mathematical background of submodularity maximization problem. Moreover, we propose other heuristic algorithms based on the distances between BSs or the utilizations of BSs that do not impose any additional signaling overhead and thus are easy to implement in practice. Extensive simulations under various practical configurations demonstrate that the proposed user association and BS operation algorithms can significantly reduce energy consumption.

Dynamic association for load balancing and interference avoidance in multi-cell networks

Next-generation cellular networks will provide higher cell capacity by adopting advanced physical layer techniques and broader bandwidth. Even in such networks, boundary users would suffer from low throughput due to severe intercell interference and unbalanced user distributions among cells, unless additional schemes to mitigate this problem are employed. In this paper, we tackle this problem by jointly optimizing partial frequency reuse and load-balancing schemes in a multicell network. We formulate this problem as a network-wide utility maximization problem and propose optimal offline and practical online algorithms to solve this. Our online algorithm turns out to be a simple mixture of inter- and intra-cell handover mechanisms for existing users and user association control and cell-site selection mechanisms for newly arriving users. A remarkable feature of the proposed algorithm is that it uses a notion of expected throughput as the decision making metric, as opposed to signal strength in conventional systems. Extensive simulations demonstrate that our online algorithm can not only closely approximate network-wide proportional fairness but also provide two types of gain, interference avoidance gain and load balancing gain, which yield 20~100% throughput improvement of boundary users (depending on traffic load distribution), while not penalizing total system throughput.We also demonstrate that this improvement cannot be achieved by conventional systems using universal frequency reuse and signal strength as the decision making metric.

Dynamic Base Station Switching-On/Off Strategies for Green Cellular Networks

In this paper, we investigate dynamic base station (BS) switching to reduce energy consumption in wireless cellular networks. Specifically, we formulate a general energy minimization problem pertaining to BS switching that is known to be a difficult combinatorial problem and requires high computational complexity as well as large signaling overhead. We propose a practically implementable switching-on/off based energy saving (SWES) algorithm that can be operated in a distributed manner with low computational complexity. A key design principle of the proposed algorithm is to turn off a BS one by one that will minimally affect the network by using a newly introduced notion of network-impact, which takes into account the additional load increments brought to its neighboring BSs. In order to further reduce the signaling and implementation overhead over the air and backhaul, we propose three other heuristic versions of SWES that use the approximate values of network-impact as their decision metrics. We describe how the proposed algorithms can be implemented in practice at the protocol-level and also estimate the amount of energy savings through a first-order analysis in a simple setting. Extensive simulations demonstrate that the SWES algorithms can significantly reduce the total energy consumption, e.g., we estimate up to 50-80% potential savings based on a real traffic profile from a metropolitan urban area.

REFIM: A Practical Interference Management in Heterogeneous Wireless Access Networks

Due to the increasing demand of capacity in wireless cellular networks, the small cells such as pico and femto cells are becoming more popular to enjoy a spatial reuse gain, and thus cells with different sizes are expected to coexist in a complex manner. In such a heterogeneous environment, the role of interference management (IM) becomes of more importance, but technical challenges also increase, since the number of cell-edge users, suffering from severe interference from the neighboring cells, will naturally grow. In order to overcome low performance and/or high complexity of existing static and other dynamic IM algorithms, we propose a novel low-complex and fully distributed IM scheme, called REFIM (REFerence based Interference Management), in the downlink of heterogeneous multi-cell networks. We first formulate a general optimization problem that turns out to require intractable computation complexity for global optimality. To have a practical solution with low computational and signaling overhead, which is crucial for low-cost small-cell solutions, e.g., femto cells, in REFIM, we decompose it into per-BS (base station) problems based on the notion of reference user and reduce feedback overhead over backhauls both temporally and spatially. We evaluate REFIM through extensive simulations under various configurations, including the scenarios from a real deployment of BSs. We show that, compared to the schemes without IM, REFIM can yield more than 40% throughput improvement of cell-edge users while increasing the overall performance by 10~107%. This is equal to about 95% performance of the existing centralized IM algorithm (MC-IIWF) that is known to be near-optimal but hard to implement in practice due to prohibitive complexity. We also present that as long as interference is managed well, the spectrum sharing policy can outperform the best spectrum splitting policy where the number of subchannels is optimally divided between macro and femto cells.

Dynamic Association for Load Balancing and Interference Avoidance in Multi-cell Networks

Next-generation cellular networks will provide higher cell capacity by adopting advanced physical layer techniques and broader bandwidth. Even in such networks, boundary users would suffer from low throughput due to severe intercell interference and unbalanced user distributions among cells, unless additional schemes to mitigate this problem are employed. In this paper, we tackle this problem by jointly optimizing partial frequency reuse and load-balancing schemes in a multicell network. We formulate this problem as a network-wide utility maximization problem and propose optimal offline and practical online algorithms to solve this. Our online algorithm turns out to be a simple mixture of inter- and intra-cell handover mechanisms for existing users and user association control and cell-site selection mechanisms for newly arriving users. A remarkable feature of the proposed algorithm is that it uses a notion of expected throughput as the decision making metric, as opposed to signal strength in conventional systems. Extensive simulations demonstrate that our online algorithm can not only closely approximate network-wide proportional fairness but also provide two types of gain, interference avoidance gain and load balancing gain, which yield 20~100% throughput improvement of boundary users (depending on traffic load distribution), while not penalizing total system throughput.We also demonstrate that this improvement cannot be achieved by conventional systems using universal frequency reuse and signal strength as the decision making metric.

Opportunistic Underlay Transmission in Multi-Carrier Cognitive Radio Systems

Underlay transmission in cognitive radio enables a secondary (unlicensed) system to utilize a frequency band of primary (licensed) system as long as the unlicensee interferes less than a certain threshold with the licensee. The secondary system needs to carefully consider not only its own channel to achieve a capacity gain by this sharing spectrum in multi-carrier systems, but also the interference channel to reduce interference at the primary receiver. In this paper, we formulate a capacity maximization problem of the secondary system under an interference-power constraint as well as a conventional transmit-power constraint, and propose an optimal power allocation policy in which we exploit a two-dimensional frequency-selectivity on both channels. Through extensive simulations, we compare the performance of optimal power allocation policy with that of equal power allocation policy and further investigate the effect of the primarys power allocation policy on the performance of the secondary system. Numerical results show that the optimal power allocation policy can achieve a higher capacity in more frequency-selective channels, compared to an equal power allocation policy. Interestingly, a water-filling policy for the primary system also gives additional opportunities to the secondary system than the equal power allocation policy.

Energy-aware hierarchical cell configuration: From deployment to operation

This paper develops an energy-aware hierarchical cell configuration framework that encompasses both deployment and operation in downlink cellular networks. Specifically, we first formulate a general problem pertaining to total energy consumption minimization while satisfying the requirement of area spectral efficiency (ASE), and then decompose it into deployment problem at peak time and operation problem at off-peak time. For the deployment problem, we start from an observation about various topologies including the real deployment of BSs that there is a strong correlation between the area covered by an additional micro BS and the increment of ASE. Under such an assumption, we prove the submodularity of ASE function with respect to micro BS deployment and propose a greedy algorithm that is shown to be a constant-factor approximation of optimal deployment. Although the greedy algorithm can be also applied as an offline centralized solution for the operation problem, we further propose online distributed algorithms with low complexity and signaling overhead to have more practical solutions. Extensive simulations based on the acquired real BS topologies and traffic profiles show that the proposed algorithms can significantly reduce the energy consumption.

SpeedBalance: Speed-scaling-aware optimal load balancing for green cellular networks

This paper considers a component-level deceleration technique in BS operation, called speed-scaling, that is more conservative than entirely shutting down BSs, yet can conserve dynamic power effectively during periods of low load while ensuring full coverage at all times. By formulating a total cost minimization that allows for a flexible tradeoff between delay and energy, we first study how to adaptively vary the processing speed based on incoming load. We then investigate how this speed-scaling affects the design of network protocol, specifically, with respect to user association. Based on our investigation, we propose and analyze a distributed algorithm, called SpeedBalance, that can yield significant energy savings.

Power Allocation for OFDM-Based Cognitive Radio Systems under Outage Constraints

This paper investigates power allocation algorithms for OFDM-based cognitive radio systems, where the intra-system channel state information (CSI) of the secondary user (SU) is perfectly known. However, due to loose cooperation between the SU and the primary user (PU), the inter-system CSI is only partially available to the SU transmitter. Two types of PUs are considered to have different capabilities. One is a dumb (Peak Interference-Power tolerable) system that can tolerate a certain amount of peak interference at each subchannel. The other is a more sophisticated (Average Interference-Power tolerable) system that can tolerate the interference from the SU as long as the average interference over all subchannels is within a certain threshold. Accordingly, we introduce an interference power outage constraint, with which the outage is maintained within a target level. The outage is here defined as the probability that peak or average interference power to the PU is greater than a given threshold. With both this interference-power outage constraint along with a transmit-power constraint, we propose optimal and suboptimal algorithms to maximize the capacity of the SU. We evaluate the spectral efficiency through extensive simulations and show that the SU can achieve higher performance (up to two times) with the more sophisticated PU than with the dumb PU.

Opportunistic Relaying in Cellular Network for Capacity and Fairness Improvement

In this paper, we study how the cooperative relaying can improve both capacity and fairness in cellular network. The capacity and fairness have a trade-off relationship, so increasing cell throughput deteriorates fairness and vice versa. First, we show that the achievable average throughput region can be enlarged by using the cooperative relaying. This enlarged region means that capacity and fairness can be improved at the same time with an adequate scheduling algorithm. Thus, secondly we propose a generalized scheduling algorithm for cooperative relaying. The proposed scheduling algorithm can improve both capacity and fairness at the expense of cooperation among users. From simulations, we show that the trade-off relationship can be surpassed and the unfairness problem in the heterogeneous channel condition can be solved by the opportunistic relaying.