Recent advances have demonstrated the potential of network MIMO (netMIMO), which combines a practical number of distributed antennas as a virtual netMIMO AP (nAP) to improve spatial multiplexing of an WLAN. Existing solutions, however, either simply cluster nearby antennas as static nAPs, or dynamically cluster antennas on a per-packet basis so as to maximize the sum rate of the scheduled clients. To strike the balance between the above two extremes, in this paper, we present the design, implementation and evaluation of FlexNEMO, a practical two-phase netMIMO clustering system. Unlike previous per-packet clustering approaches, FlexNEMO only clusters antennas when client distribution and traffic pattern change, as a result being more practical to be implemented. A medium access control protocol customized for uplink transmissions is then designed to allow the clients at the center of nAPs to have a higher probability to gain uplink access opportunities, but still ensure long-term fairness among clients. By combining on-demand clustering and priority-based access control, FlexNEMO not only improves antenna utilization, but also optimizes the channel condition for every individual client. We evaluated our design via both test bed experiments on USRPs and trace-driven emulations. The results demonstrate that FlexNEMO can deliver 94.7% and 93.7% throughput gains over static antenna clustering in a 4-antenna test bed and 16-antenna emulation, respectively.

Recent advances have demonstrated the potential of network MIMO (netMIMO), which combines a practical number of distributed antennas as a virtual netMIMO AP (nAP) to improve spatial multiplexing of an WLAN. Existing solutions, however, either simply cluster nearby antennas as static nAPs, or dynamically cluster antennas on a per-packet basis so as to maximize the sum rate of the scheduled clients. To strike the balance between the above two extremes, in this paper, we present the design, implementation and evaluation of FlexNEMO, a practical two-phase netMIMO clustering system. Unlike previous per-packet clustering approaches, FlexNEMO only clusters antennas when client distribution and traffic pattern change, as a result being more practical to be implemented. A medium access control protocol is then designed to allow the clients at the center of nAPs to have a higher probability to gain access opportunities, but still ensure long-term fairness among clients. By combining on-demand clustering and priority-based access control, FlexNEMO not only improves antenna utilization, but also optimizes the channel condition for every individual client. We evaluated our design via both testbed experiments on USRPs and trace-driven emulations. The results demonstrate that FlexNEMO can deliver 94.7% and 93.7% throughput gains over static antenna clustering in a 4-antenna testbed and 16-antenna emulation, respectively.

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.

Multi-user multiple input and multiple output (MU-MIMO) is one predominate approach to improve the wireless capacity. However, since the aggregate capacity of MU-MIMO heavily depends on the channel correlations among the mobile users in a beamforming group, unwisely selecting beamforming groups may result in reduced overall capacity, instead of increasing it. How to select users into a beamforming group becomes the bottleneck of realizing the MU-MIMO gain. The fundamental challenge for user selection is the large searching space, and hence there exists a tradeoff between search complexity and achievable capacity. Previous works have proposed several low complexity heuristic algorithms, but they suffer a significant capacity loss. In this paper, we present a novel MU-MIMO MAC, called SIEVE. The core of SIEVE design is its scalable multi-user selection module that provides a knob to control the aggressiveness in searching the best beamforming group. SIEVE maintains a central database to track the channel and the coherence time for each mobile user, and largely avoids unnecessary computing with a progressive update strategy. Our evaluation, via both small-scale testbed experiments and large-scale trace-driven simulations, shows that SIEVE can achieve around 90% of the capacity compared to exhaustive search.

Mobile ad hoc networks (MANETs) are ideal for situations where a fixed infrastructure is unavailable or infeasible. Today's MANETs, however, may suffer from network partitioning. This limitation makes MANETs unsuitable for applications such as crisis management and battlefield communications, in which team members might need to work in groups scattered in the application terrain. In such applications, intergroup communication is crucial to the team collaboration. To address this weakness, we introduce in this paper a new class of ad-hoc network called Autonomous Mobile Mesh Network (AMMNET). Unlike conventional mesh networks, the mobile mesh nodes of an AMMNET are capable of following the mesh clients in the application terrain, and organizing themselves into a suitable network topology to ensure good connectivity for both intra- and intergroup communications. We propose a distributed client tracking solution to deal with the dynamic nature of client mobility, and present techniques for dynamic topology adaptation in accordance with the mobility pattern of the clients. Our simulation results indicate that AMMNET is robust against network partitioning and capable of providing high relay throughput for the mobile clients.

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in an MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: Its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7× and 2.3×, for 2-antenna and 3-antenna APs, respectively.

Exchanging the channel state information (CSI) in a multiuser WLAN is considered an extremely expensive overhead. A possible solution to reduce the overhead is to notify the analog value of the CSI, which is also known as analog channel feedback. It however only allows nodes to overhear an imperfect channel information. While some previous studies have theoretically analyzed the performance of analog channel feedback, this work aims at addressing issues of realizing it in practice and empirically demonstrating its effectiveness. Our prototype implementation using USRP-N200 shows that analog channel feedback produces a small error comparable to that of estimating CSI using reciprocity, but however can be applied to more general scenarios.

Adaptation of modulation and transmission bit-rates for video multicast in a multirate wireless network is a challenging problem because of network dynamics, variable video bit-rates, and heterogeneous clients who may expect differentiated video qualities. Prior work on the leader-based schemes selects the transmission bit-rate that provides reliable transmission for the node that experiences the worst channel condition. However, this may penalize other nodes that can achieve a higher throughput by receiving at a higher rate. In this work, we investigate a rate-adaptive video multicast scheme that can provide heterogeneous clients differentiated visual qualities matching their channel conditions. We first propose a rate scheduling model that selects the optimal transmission bit-rate for each video frame to maximize the total visual quality for a multicast group subject to the minimum-visual-quality-guaranteed constraint. We then present a practical and easy-to-implement protocol, called QDM, which constructs a cluster-based structure to characterize node heterogeneity and adapts the transmission bit-rate to network dynamics based on video quality perceived by the representative cluster heads. Since QDM selects the rate by a sample-based technique, it is suitable for real-time streaming even without any preprocess. We show that QDM can adapt to network dynamics and variable video-bit rates efficiently, and produce a gain of 2-5 dB in terms of the average video quality as compared to the leader-based approach.

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in a MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7x and 2.3x, for 2-antenna and 3-antenna APs respectively.

Because of marketing demand of user-centric video dissemination, promoting video content via interactive distribution platforms has received considerable attention recently. In addition, the preferences of clients are heterogenous and dynamic, effectively utilizing the network bandwidth to distribute user-centric content becomes a challenging problem, especially when the number of users in the systems scale up. Therefore, we propose a P2P-based distribution platform, called Bulldozer, which enable clients to cooperatively propagate user-centric video content in a large scale environment, and better utilizes peers upload bandwidth to share the server traffic load. Bulldozer consists of two components: 1) Clip Scheduling phase schedules the playing order of videos for each individual user, and 2) Bandwidth Allocation phase allocates users upload bandwidths to distribute videos among clients. Finally, simulation results show that Bulldozer can effectively reduce the peak traffic load of the server.