Unlike conventional omnidirectional antennas, switched beam antennas exploit antenna arrays and signal processing techniques to focus energy in a specific beam-width and orientation. Recent research has shown that access points of a WLAN can exploit such switched beam antennas to increase the overall network capacity. The achievable sum rate of a WLAN with switch beam antennas is however mainly determined by how each AP selects its beam, including the orientation and width, and how each client associates with a proper AP. The goal of this paper is to solve the Joint Beam configuration and Client association (JBC) problem such that the sum rate of all clients in the network can be maximized. We formulate the JBC problem as a mixed integer linear programming model, and propose a 2-approximation algorithm to solve it. Our proposed algorithm has two distinctive properties: 1) it can be realized as a distributed protocol that allows the APs to configure their beams without the help of a central coordinator, and 2) it can be generally applied both in specific scenarios, where exact client locations are known, and in uncertain scenarios, where only geographic client distribution is given. Finally, we adjust the sum-rate maximization algorithm to the throughput maximization algorithm, which further takes medium sharing among clients into account. The simulation results show that the proposed algorithm outperforms both WLANs using omnidirectional antennas and other heuristics using switched beam antennas.

While recent works on multiuser MIMO (MU-MIMO) mainly focus on boosting the throughput by enabling concurrent transmissions, less attention is paid to recovering concurrent erroneous streams. We however notice that uplink MU-MIMO is especially vulnerable to errors because error in any stream corrupts most of the other concurrent streams. Even worse, carrier sense and rate adaptation become more challenging in MU-MIMO, which further decreases reliability. Existing systems however recover errors by retransmitting all the streams in a corrupted packet, thereby taking away the significant performance benefit of MU-MIMO. To effectively harness the ideal gain of MU-MIMO, we develop Concurrent Packet Recovery (CPR), a recovery protocol customized for MU-MIMO. It has two distinctive features: (i) It judiciously selects the minimum number of streams to be retransmitted to support successful decoding; (ii) during retransmission, it utilizes the full degrees of freedom by allowing new streams to be sent in parallel. Our evaluation via testbed experiments and trace-driven simulations shows that CPR can efficiently recover both normal losses and collisions. For three-antenna AP scenarios, the throughput gain is up to 31.1 percent when no hidden terminal exists, and is 3.16× when 20 percent pairs of contending clients are hidden terminals.