In this paper, we study an opportunistic scheduling and adaptive modulation scheme for a wireless network with an XOR network coding scheme, which results in a cross-layer problem for MAC and physical layers. A similar problem was studied in [2] which considered an idealized system with the Shannon capacity. They showed that it may not be optimal for a relay node to encode all possible native packets and there exists the optimal subset of native packets that depends on the channel condition at the receiver node of each native packet. In this paper, we consider a more realistic model than that of [2] with a practical modulation scheme such as M-PSK. We show that the optimal policy is to encode native as many native packets as possible in the network coding group into a coded packet regardless of the channel condition at the receiver node for each native packet, which is a different conclusion from that of [2]. However, we show that adaptive modulation, in which the constellation size of a coded packet is adjusted based on the channel condition of each receiver node, provides a higher throughput than fixed modulation, in which its constellation size is always fixed regardless of the channel condition at each receiver node.

In this paper, we study an opportunistic scheduling scheme for the TDMA wireless network with relay stations. We model the time-varying channel condition of a wireless link as a stochastic process. Based on this model, we formulate an optimization problem for the opportunistic scheduling scheme that maximizes the expected system throughput while satisfying the QoS constraint of each user. In the opportunistic scheduling scheme for the system without relay stations, each user has only one communication path between the base station and itself, and thus only user selection is considered. However, in our opportunistic scheduling scheme for the system with relay stations, since there may exist multiple paths between the base station and a user, not only user selection but also path selection for the scheduled user is considered. In addition, we also propose an opportunistic time-sharing method for time-slot sharing between base station and relay stations. With the opportunistic time-sharing method, our opportunistic scheduling provides opportunistic resource sharing in three places in the system: user selection in a time-slot, path selection for the selected user, and time-slot sharing between base station and relay stations. Simulation results show that as the number of places that opportunistic resource sharing is applied to increases, the performance improvement also increases.

In our previous work [2], we proposed a new concept of utility functions for rate control in communication networks. Unlike conventional utility-based rate control in which the utility function of each user is defined as a function of its transmitting data rate, in [2], we defined the utility function of each user as a function of not only its transmitting data rate but also it receiving data rate. The former is called a session-level utility function and the latter is called a user-level utility function. The user-level utility function reflects the satisfaction with the service of a user with two-way communication, which consists of transmitting and receiving sessions, better than the session-level utility function, since user's satisfaction depends on not only the satisfaction with its transmitting session but also that for its receiving session. In [2], an algorithm that required each user to know the exact utility function of its correspondent was developed. However, in some cases, this information might not be available due to some reasons such as security and privacy issues, and in such cases, the algorithm developed in [2] cannot be used. Hence, in this paper, we develop a new distributed algorithm that does not require each user to know the utility function of its correspondent. Numerical results show that our new algorithm, which does not require the utility information of the correspondent, converges to the same solution to that with the algorithm that requires the utility information of the correspondent.

In most cases in wireless networks, a user has a two-way communication that consists of two sessions: uplink and downlink sessions, and its overall satisfaction to the communication depends on the quality of service of both sessions. However, in most previous approaches in wireless resource allocation, the satisfactions of a user for its uplink and downlink sessions are modeled separately and treated independently, which fails to accurately model user's overall satisfaction to its communication. Hence, in this paper we model user's overall satisfaction to its communication considering both its uplink and downlink sessions. To this end, we propose a novel concept for a utility function to model user's overall satisfaction to its communication, which is called a user-level utility function, considering user's satisfaction to uplink and downlink sessions jointly. To show the appropriateness of our approach, we apply our user-level utility functions to scheduling problems in TDMA wireless networks and show the performance improvement of our approach over the traditional approach that does not treat uplink and downlink sessions of a user jointly.

In this paper, we propose an energy efficient MAC protocol for wireless sensor networks. In sensor networks, reducing energy consumption is one of the critical issues for extending network lifetime. One good solution to resolve this issue is introducing listen-sleep cycles, allowing sensor nodes to turn their transceiver off during sleep periods, which was adopted by S-MAC [1]. However, in S-MAC, due to the synchronized scheduling, transmission collisions will increase in heavy traffic situations, resulting in energy waste and low throughput. Hence, in this paper, we propose probabilistic scheduled MAC (PS-MAC), in which each node determines ‘listen’ or ‘sleep’ pseudo-randomly based on its own pre-wakeup probability and pre-wakeup probabilities of its neighbor nodes in each time slot. This allows the listen-sleep schedule of nodes in each transmitter and receiver pair to be synchronized, while maintaining those of the rest of nodes to be asynchronous. Therefore, collisions can be reduced even under heavy traffic conditions, resulting in reduced energy waste and high throughput. In addition, by dynamically adjusting the pre-wakeup probabilities of sensor nodes based on the change of the network environment, system throughput and latency can be further improved. Simulation results show that PS-MAC provides significant energy savings, low delay, and high network throughput.

We use the network utility maximization (NUM) framework to create an efficient and fair medium access control (MAC) protocol for wireless networks. By adjusting the parameters in the utility objective functions of NUM problems, we control the tradeoff between efficiency and fairness of radio resource allocation through a rigorous and systematic design. In this paper, we propose a scheduling-based MAC protocol. Since it provides an upper-bound on the achievable performance, it establishes the optimality benchmarks for comparison with other algorithms in related work.

In this paper, we design an adaptive mobility-supporting MAC (AM-MAC) protocol for mobile sensor networks. In our protocol, as in S-MAC [1], each virtual cluster that consists of a subset of sensor nodes has its own listen-sleep schedule. Hence, when a mobile sensor node moves into a new virtual cluster, it needs to adapt to the listen-sleep schedule of the corresponding virtual cluster in a timely and energy efficient manner. To this end, in our protocol, we utilize schedule information on border nodes between virtual clusters. This allows us to implement fast and energy efficient listen-sleep schedule adaptation for mobile nodes, which consists of two main functions: energy efficient secondary listen period and smart scheduling adaptation. Simulation results show that our protocol can provide fast schedule adaptation while achieving energy efficiency.