In this paper, we consider interference networks where the connectivity is known globally while the channel gains are known up to a particular distance from each node. In this setting, we provide a new achievability, called Maximal Clique Scheduling (MCS), which is a special case of Maximal Independent Graph Scheduling (MIG Scheduling) proposed earlier. The strategy is evaluated using the notion of normalized sum rate which is a metric to evaluate performance of networks with mismatched knowledge. The achievable normalized sum rate of the proposed MCS strategy is easier to analyze for certain classes of networks and can be used to bound the normalized sum rate of MIG Scheduling. We investigate the normalized sum rate achieved by MCS for two classes of networks. The first class is formed by interference networks where each link is connected with probability $p$. The second class is derived from Wyner 1-D model of placements of base stations and mobile nodes. We find that increasing knowledge about the network leads to increasing normalized sum-rate. However, in a random network, the increase is slower as compared to Wyner network because most nodes are far away from a node and hence learning more helps less until the whole network is known.

In most wireless networks, nodes have only limited local information about the state of the network, which includes connectivity and channel state information. With limited local information about the network, each node’s knowledge is mismatched; therefore, they must make distributed decisions. In this thesis, we pose the following question - if every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While link scheduling answers the above question for point-to-point physical layers which are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced code design, as suggested by results in network information theory. To make progress on this challenging problem, we propose two constructive distributed algorithms, one conservative and one aggressive, which achieve rates higher than link scheduling based on interference avoidance, especially if each node knows more than one hop of network state information. Both algorithms schedule sub-networks such that each sub-network can employ advanced interference-embracing coding schemes to achieve higher rates. Our innovation is in the identification, selection and scheduling of sub-networks, especially when sub-networks are larger than a single link. Using normalized sum-rate as the metric of network performance, we prove that the proposed conservative sub-network scheduling algorithm is guaranteed to have performance greater than or equal to pure coloring-based link scheduling. In addition, the proposed aggressive sub-network scheduling algorithm is shown, through simulations, to achieve better normalized sum-rate than the conservative algorithm for several network classes. Our results highlight the advantages of extending the design space of possible scheduling strategies to include those that leverage local network information.