-The IEEE 802.11 standard specifies both radio and MAC protocol design. We observe that its CSMA protocol helps avoid much of co-channel interference by sharing radio resources in time at the potential expense of degraded network performance. Due to the coupling between the physical and MAC layers, conventional frequency allocation methods for typical cellular networks cannot be applied directly to the 802.11 networks.In this paper, by focusing on interactions among access points, we formulate the channel assignment problem for the 802.11 network, considering the traffic load at the MAC layer, and prove that the problem is NP-complete. In light of computational complexity, a heuristic algorithm is proposed and analyzed. The algorithm is then applied to two cellular settings with known optimal assignments for verification. For one of the settings, the proposed technique generates the optimal channel assignment. As for the second case of a large network, although only a suboptimal solution is obtained by the algorithm, it is shown to be excellent. Thus, as the 802.11 networks are widely deployed, the proposed method can serve as a valuable tool for frequency planning of networks with non-uniform coverage and load.

We propose a blind sequence estimator( BSE ) that is well suited for short burst communication formats used in many wireless TDMA systems. It is based on Per Survivor Processing( PSP ) Maximum Likelihood Sequence Estimation( MLSE ). However, the initial channel estimates needed for conventional MLSE, are obtained Oliridly from the on-set of a burst. This provides PSP with nearconvergence initial channel estimates without the help of training sequences. Thus, this approach is effective for short burst TDMA wireless communication, since it eliminates the overhead associated with training which is significant in short burst formats. For fast time-varying frequency selective wireless channels, the proposed BSE performs similarly compared to conventional PSP MLSE with training sequences. This is mainly because the need for channel tracking limits the usefulness of training sequences.