Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose a novel electricity load scheduling which, unlike previous works, jointly considers the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. Under the assumption of the collaborative system where the customers agree to maximize the social welfare of the power system, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare. Through numerical results, we show when the energy trading leads to an increase in the social welfare in various usage scenarios.

Future generation smart grids will allow customers to trade energy bidirectionally. Specifically, each customer will be able to not only buy energy from the aggregator during its peak hours but also sell its surplus energy during its off-peak hours. In these emerging energy trading markets, a key component will be the deployment of effective energy billing schemes which consider the customers residential load scheduling. In this paper, we consider a residential load scheduling problem with bidirectional energy trading. Compared with the previous work, in which customers are assumed to be obedient and agree to maximize the social welfare of the smart grid system, in this paper, we consider a non-collaborative approach, where consumers are self-interested. We model the energy scheduling problem as a non-cooperative game, where each customer determines its load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers, we propose a novel tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. We also propose a distributed energy scheduling algorithm that converges to the unique Nash equilibrium of the studied non-cooperative game. Through the numerical results, we study the impact of the proposed tiered billing scheme on the selfish customers' behavior and on their incentives to participate in the energy trading market.

Electric vehicles (EVs) will play an important role in the future smart grid because of their capabilities of storing electrical energy in their batteries during off-peak hours and supplying the stored energy to the power grid during peak hours. In this paper, we consider a power system with an aggregator and multiple customers with EVs and propose novel electricity load scheduling algorithms which, unlike previous works, jointly consider the load scheduling for appliances and the energy trading using EVs. Specifically, we allow customers to determine how much energy to purchase from or to sell to the aggregator while taking into consideration the load demands of their residential appliances and the associated electricity bill. We propose two different approaches: a collaborative and a non-collaborative approach. In the collaborative approach, we develop an optimal distributed load scheduling algorithm that maximizes the social welfare of the power system. In the non-collaborative approach, we model the energy scheduling problem as a non-cooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. In order to resolve the unfairness between heavy and light customers in the non-collaborative approach, we propose a tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels. In both approaches, we also consider the uncertainty in the load demands, with which customers' actual energy consumption may vary from the scheduled energy consumption. To study the impact of the uncertainty, we use the worst-case-uncertainty approach and develop distributed load scheduling algorithms that provide the guaranteed minimum performances in uncertain environments. Subsequently, we show when energy trading leads to an increase in the social welfare and we determine what are the customers' incentives to participate in the energy trading in various usage scenarios including practical environments with uncertain load demands.

We study an opportunistic subchannel scheduling and transmission mode selection problem for the OFDMA system with device-to-device (D2D) communication. We allow D2D users to opportunistically select its transmission mode between two transmission modes: direct transmission between D2D users (direct one- hop transmission) and indirect transmission through the BS (indirect two-hop transmission). We develop a framework with which opportunistic transmission mode selection can be modeled as opportunistic subchannel scheduling, which enables our problem to be reduced to an opportunistic subchannel scheduling problem. We formulate a stochastic optimization problem that aims to maximize the average sum-rate of the system, while satisfying the quality-of-service (QoS) requirement of each user. By solving the problem, we develop an optimal opportunistic subchannel scheduling algorithm, which enables us to perform both subchannel scheduling and transmission mode selection opportunistically.

In this paper, we study an opportunistic resource scheduling problem for the relay-based OFDMA cellular network where relay stations (RSs) perform opportunistic network coding with downlink and uplink sessions of a mobile station (MS). To this end, we consider time-division duplexing (TDD) where each time-slot is divided into three phases according to the type of transmitter nodes, i.e., the base station (BS), MSs, and RSs. Moreover, to improve the flexibility for resource allocation, we allow dynamic TDD, in which the time duration of each phase in each time-slot can be adjusted. For opportunistic network coding, we introduce a novel model for network coding aware RSs with which an opportunistic network coding problem can be reduced to an opportunistic subchannel scheduling problem. We formulate an optimization problem that aims at maximizing the average weighted-sum rate for both downlink and uplink sessions of all MSs, while satisfying the quality-of-service (QoS) requirements of each MS. By solving it, we develop a resource scheduling algorithm that optimally and opportunistically schedule subchannel, transmission power, network coding, and time duration of each phase in each time-slot. Through the numerical results, we study how each of network coding strategy and dynamic TDD affects the network performance with various network environments.

In femtocell networks, due to their small cell size, we can achieve higher spatial diversity from the channel reuse between multiple femtocells. In addition, if femtocells are operated on OFDMA systems, each subchannel can be reused separately among femtocells by carefully treating inter-cell interferences. However, due to a large number of femtocells and their uncoordinated and irregular deployment, we need to treat intercell interferences very carefully in OFDMA-based fmetocell networks, which make developing efficient resource allocation schemes more difficult. In this paper, we study a subchannel allocation problem that aims at maximizing the sum utility of the OFDMA-based femtocell network. Since the problem is formulated as a nonlinear integer programming, which is inherently difficult to solve, we propose a suboptimal subchannel allocation algorithm. The proposed subchannel allocation algorithm consists of two steps: calculating the number of subchannels that should be granted to each femtocell to maximize the sum utility and finding actual subchannel allocation that achieves the granted number of subchannels for each femtocell. Numerical results show that the proposed subchannel allocation algorithm provides near-optimal performance.

In this paper, we study an opportunistic subchannel scheduling problem for the relay-based OFDMA cellular network where relay stations (RSs) can perform opportunistic network coding with downlink and uplink sessions of a mobile station (MS). In wireless networks, network coding is one of promising techniques to improve network performance in various aspects. However, previous studies did not provide optimal opportunistic resource allocation solutions for the OFDMA networks with network coding-aware RSs as well as they did not consider practical duplexing schemes for the transmission of network coded data. In this paper, to allow network coding with downlink and uplink sessions at RSs, we consider a time-division duplexing (TDD) based time-slot structure. We formulate a stochastic optimization problem to maximize the average weighted-sum rate for both downlink and uplink sessions. Then, by solving the problem, we provide an optimal opportunistic subchannel scheduling algorithm that can opportunistically use network coding with considering time-varying channel states.

In this paper, we study a utility based flow control problem for a communication network. In most previous works on utility based flow control, the utility function of each user, which represents its satisfaction to the allocated data rate, is assumed to be fixed. This implies that the degree of the rate requirement of each user is assumed to be fixed over the entire duration of its session. However, in the communication network, many services are variable rate services, i.e., the degree of their rate requirement varies over time. Hence, in this paper, we allow the degree of the rate requirement of each user to change over time and model it by using a stochastic utility function that varies stochastically according to the variation of the degree of its rate requirement. We formulate a stochastic optimization problem with stochastic utility functions that aims at maximizing the average network utility while satisfying the constraint on the link capacity. By solving the stochastic optimization problem, we develop a distributed flow control algorithm that converges to the optimal rate allocation.

So far, many researches on network coding are performed with higher layer protocols such as MAC, routing, and flow control protocols without consideration of physical layer issues such as channel conditions of links. However, in wireless networks, the consideration of properties at the physical layer is important to improve system performance. Hence, in this paper, we study an opportunistic scheduling and adaptive modulation problem for wireless networks with network coding, which is a joint problem for MAC and physical layers. A similar problem was studied in considering an idealized system in which the data rate of each link is modeled with the Shannon capacity. They showed that to maximize the throughput of a transmission, the optimal subset of native packets that are encoded within a coded packet should be selected based on the channel condition at the destination for each native packet. Moreover, they also showed that it may not be the optimal selection to encode all possible native packets within a coded packet. In this paper, we consider a more realistic model than that of with practical modulation schemes such as M-PSK and MQAM. We show that the optimal policy that maximizes the throughput of a transmission is to encode all available native packets within a coded packet regardless of the channel condition at the destination for each native packet, which is a different conclusion from that of. However, we show that adaptive modulation, in which its constellation size in a coded packet is adjusted based on the channel condition of each destination node, provides a higher throughput than the scheme with fixed modulation, in which its constellation size is always fixed regardless of the channel condition at each destination node.

In this paper, we study an opportunistic power scheduling problem in relay-based OFDMA cellular networks. In most previous works on relay-based cellular networks, scheduling at relay stations (RSs) is not allowed. Hence, each RS should immediately transmit all the received data from the base station (BS) in the first phase in a time-slot to mobile stations (MSs) in the second phase in the same time-slot. In this strategy, the effective data rate of the two-hop transmission (BS-RS and RS-MS) is limited to the rate of the link with worse channel state among two links. Hence, in those systems, even though opportunistic scheduling is allowed in the BS, time-varying channel state of each link cannot be fully exploited, which may result in wastes of radio resources. However, if opportunistic scheduling is allowed not only at the BS but also at each RS, the BS and each RS can fully exploit time-varying channel state of each link, so that more efficient radio resource allocation is possible. We formulate a stochastic optimization problem that aims at maximizing the average sum-rate of relay-based networks where the time-varying wireless channel is modeled as a stochastic process. By solving the problem, we develop an optimal power scheduling algorithm which can be implemented in a distributed manner at the BS and each RS. Simulation results show that by allowing opportunistic scheduling at both the BS and RSs, we can improve system performance more significantly.