Collaborative indoor positioning requires maps with explicitly defined spatial connectivity. This paper presents a framework for constructing indoor map layouts using spatial entities and reachability structures. We introduce the Floor-Group-Area (FGA) Matrix Problem to visualize and optimize layout connectivity through column reordering. The framework exports 1bpp OGMs and spatial metadata in JSON. In user evaluations, trained participants completed full layouts of a four-story museum in 20 minutes, with all computationally intensive algorithms running in few seconds on a standard PC.

By the infrastructure of cellular network changing, the conventional methods has became not be adequate. As an alternative, a probabilistic model for dynamic input aiming on signals from multiple cellular station is proposed in this study, and the feasibility was verified. The result from the experiment can be shown a little petty, but if one experiments in a better environment and utilizes LTE and 5G network, about 20~30m of the positioning error can be expected.

In this paper, we develop a user Quality of Service (QoS) aware adaptive Radio Frequency (RF) chain switching technique that dynamically adapts the number of active RF chains to minimize the total power consumption of the base station (BS). Specifically, we first formulate an optimization problem to minimize the total power consumption under the throughput and Block Error Rate (BLER) requirements of the users of the BS. Due to the prohibitive complexity of exhaustive algorithm achieving an optimal solution, we propose a practically implementable heuristic algorithm, which jointly optimizes the number of active RF chains, frequency and time resources. Simulation results demonstrate that the proposed algorithm can achieve significant savings in total power consumption of the BSs compared to the conventional static All-On scheme wherein the activity of RF chains is not adapted.

Given the significant energy consumption in operating base stations (BSs), improving their energy efficiency is an important problem in cellular networks. To this end, this paper proposes a novel framework, called DCR (dynamic cell reconfiguration) that dynamically adjust the set of active BSs and user association according to user traffic demand for energy conservation. In order to overcome prohibitive computational complexity in finding an optimal solution, we take an approach to design simple yet effective algorithms. We demonstrate that the proposed framework is not only computationally efficient but also can achieve the performance close to the optimum solution from an exhaustive search. Through simulations based on a real dataset of BS topology and utilization, we show that DCR can yield about a 30-40% reduction compared to the conventional static scheme where all BSs are always turned on.

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.

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.

Greening effect in interference management (IM), a way of enhancing spectrum sharing via intelligent transmit power control, can be achieved by the fact that as BSs moderately reduce their transmit powers, the performance degradation decreases slower than linearly, yet a considerable overall energy saving is expected due to transmit powers' exerting influence on operational power. This paper investigates the impact of different spatial and/or temporal power sharing policies for a given system-wide power budget in IM schemes. We develop an optimization-theoretic IM framework on cellular network greening, from which we first develop four IM schemes governed by different power sharing: no sharing, only temporal sharing, only spatial sharing, and both spatial and temporal sharing. Through extensive simulations, including a real BS deployment in Manchester city, United Kingdom, we obtain the following interesting observations: (i) the gains both from performance and power saving are obtained by adopting the spatial and/or temporal power sharing policies, (ii) tighter greening regulation (i.e., smaller total power budget) leads to higher spatio-temporal power sharing gain than IM gain, (iii) spatial power sharing significantly excels temporal one in terms of power saving, and (iv) higher greening efficiency can be achieved as the cell size becomes smaller.

In order to utilize network resources efficiently and reduce regional congestion, associating mobile stations (MSs) with proper base stations (BSs) is of crucial importance in wireless cellular networks. There have been several load-aware proposals in literature, where most are classified into so-called closed-form approaches. In such approaches, each MS independently and deterministically selects the BS which is expected to provide the highest throughput. The throughput is estimated by a closed-form equation based on the assumption of the Proportional Fair (PF) user scheduler that ensures temporal fairness. However, the closed-form approaches do not perform well when the closed-form equation is not available, e.g., general α-fair user scheduler, where temporal fairness is not guaranteed, or deterministic BS association may make wrong decisions, e.g., under the dynamics of mobility or flow arrivals/departures. In this paper, we propose a novel BS association scheme, called ViSE (Virtual Scheduling based Emulation) to tackle such challenges. It emulates an optimal BS association by running a notion of virtual scheduler, and each MS randomly determines its associated BS with the probability proportional to the throughput virtually allocated by the virtual scheduler. We demonstrate through extensive simulations under various practical scenarios that ViSE outperforms the existing algorithms in terms of user schedulers with diverse fairness and robustness to network dynamics.

Greening effect in interference management (IM), which is a technology to enhance spectrum sharing via intelligent BS transmit power control, can be achieved by the fact that even small reduction in BS transmit powers enables considerable saving in overall energy consumption due to their exerting influence on operational powers. In this paper, we study the impact of power sharing policies in IM schemes on cellular network greening, where different spatio-temporal power sharing policies are considered for a fixed system-wide power budget. This study is of great importance in that the pressure on the CO2emission limit per nation increases, e.g., by Kyoto protocol, which will ultimately affect the power budget of a wireless service provider. We propose optimization theoretic IM frameworks with greening, from which we first develop four IM schemes with different power sharing policies. Through extensive simulations under various configurations, including a real BS deployment in Manchester city, United Kingdom, we obtain the following interesting observations: (i) tighter greening regulation (i.e., the smaller total power budget) leads to higher spatio-temporal power sharing gain than IM gain, (ii) spatial power sharing significantly excels temporal one, and (iii) more greening gain can be achieved as the cell size becomes smaller.

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.

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.

Achieving sufficient spatial capacity gain through the use of small cells requires careful consideration of inter-cell interference (ICI) management via BS power coordination coupled with user scheduling inside cells. Optimal algorithms are known to be difficult to implement due to high computation and signaling overhead. This study proposes joint pattern-based ICI management and user scheduling algorithms that are practically implementable. The key idea is to decompose the original problem into two sub-problems in which ICI management is run at a slower time scale than user scheduling. We empirically show that even with such a slow tracking of system dynamics at the ICI management part, the decomposed approach achieves a considerable performance increase compared to conventional universal reuse schemes.

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.

In this paper, we propose a scheduling framework for heterogeneous traffic in OFDMA-based wireless systems. The proposed scheduling algorithm not only satisfies the QoS requirements of the real-time traffic but also maximizes the utility of the non real-time traffic. Step-by-step approach is used to achieve these two objectives simultaneously with low complexity and traffic class prioritization. A well-known bipartite matching algorithm and a standard gradient scheduling algorithm are adopted for the QoS scheduling of the real-time traffic and for the utility maximization scheduling of the non real-time traffic, respectively. Moreover, a noble beta deadline parameter is introduced to control the balance between the QoS provisioning and the diversity gain. Extensive simulation results in various scenarios are provided to demonstrate the good features of our scheduling framework.

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 primary's 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.

Achieving sufficient spatial capacity gain by having small cells requires careful treatment of inter-cell interference (ICI) management via BS power coordination coupled with user scheduling inside cells. Optimal algorithms have been known to be hard to implement due to high computation and signaling overheads. We propose joint pattern-based ICI management and user scheduling algorithms that are practically implementable. The basic idea is to decompose the original problem into two sub-problems, where we run ICI management at a slower time scale than user scheduling. We empirically show that even with such a slow tracking of system dynamics at the ICI management part, the decomposed approach achieves high performance increase, compared to a conventional universal reuse scheme.

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.

The introduction of relays into legacy cellular networks is gaining more of an interest in the 4th generation (4G) wireless systems. In the presence of relays, simultaneous transmission of both base stations (BS) and relay stations (RS) on the same resources is possible, and consequently much higher throughput can be achieved. However, determining the set of links which will be turned on at the same time is usually considered a NP-complete combinatorial problem. Moreover, it requires cooperation between BSs and RSs of independent cells. In such context, we briefly address the difficulties of spatial resource reuse from the graph-theoretical perspective. Next, our focus shifts to developing a simple but efficient radio resource management algorithm which enables the spatial resource reuse, the pricing- based radio resource management (PRRM) strategy. The PRRM performs spatial reuse for interference-free users operating in the high signal-to-interference-and-noise ratio (SINR) region, while guaranteeing the signal quality of interference-susceptible users usually located near the coverage boundary. By applying the PRRM, we evaluate the potential benefits of the spatial resource reuse.

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.

Most of up-to-date wireless data systems are based on the OFDM technology and employ the opportunistic scheduling to exploit channel itself fully. Thus, feedback reduction in multiuser multi-carrier systems becomes more of an issue due to the large amount of feedback required to pass to the base station. In this paper, we propose a novel feedback reduction scheme preserving the essential of multi-user diversity. In our proposed scheme, active users determine their feedback amount based on the feedback efficiency factor in a distributed manner. The objective is to reduce the feedback load remarkably while achieving almost the same performance to the full feedback condition. Our proposed scheme offers several advantages over existing ones. First, it does not distort the property of the scheduling policies. Second, total feedback load can be explicitly controlled to a target level regardless of the number of users in a system by adjusting each user's feedback load adaptively.

In this paper, conventional bandwidth allocation schemes in ethernet passive optical network (EPON) are shown to suffer from poor utilization under the nonuniform traffic, particularly as the number of ONUs, guard time and round-trip time increase. To resolve this problem, we propose a new scheme which intelligently allocates a timeslot in consideration of other ONUs' queue occupancy, instead of strictly enforcing maximum timeslot size. The analysis and simulation results show that the proposed scheme can provide significantly higher utilization than the conventional schemes and support max-min fairness under the nonuniform traffic.