The widespread proliferation of wireless sensor networks is revolutionizing our capabilities of monitoring and controlling the environment. The wireless connection of spatially distributed sensors, controllers and actuators poses challenges to the control system, due to packet drops, delays and measurements quantization, as well as to the wireless network resource allocator. These challenges push for a cross-layer design of communication and estimation/control systems. In this paper, assuming a TCP-like protocol between controller and actuator, we solve the problem of optimum control around a target state for a stable system in case of both packet drops and signal quantization. Generalization for unstable systems is also given in case of negligible quantization error. Moreover we propose a general framework for cross-layer optimization of signal quantization and network resource allocation. Here our main contributions are on i) quantizer design (how many bits to allocate for signal quantization), ii) network resource allocation (bit-rate for each radio link) and iii) choice of the transmission mode (constellation and channel coding rate). As application example, we consider a simple scalar, stable system and compare network resource allocation in the presence of i) low-cost sensors using a fix modulation and ii) long-term future sensors capable of rate adaptation. Interestingly, almost optimal control is achievable with small bandwidth transmissions and simple BPSK, supporting the use of low-cost sensors in applications dealing with state control around a target state trajectory.

The wireless connection of spatially separated sensors, controllers and actuators poses challenging problems to the control system, due to packet drops, delays and measurements quantization, as well as to the wireless network resource allocator. This pushes for a cross-layer design of communication and estimation/control systems. Assuming a TCP-like protocol between controller and actuator, we solve the problem of optimum control around a target state for a stable system in case of both packet drops and signal quantization. Generalization for unstable systems is also given for large bandwidth transmissions. Next, we derive the limiting behavior of the system in the infinite horizon and propose a general framework for cross-layer optimization of signal quantization and network resource allocation. As an example of application, we consider a simple scalar, stable system and compare network resource allocation in the presence of i) low-cost sensors using a fix modulation and ii) long-term future sensors capable of rate adaptation. Interestingly, almost optimal control is achievable by small bandwidth transmissions using a simple BPSK, supporting the use of low-cost sensors in applications dealing with state control in stable systems.

We consider a multiuser MIMO-OFDM downlink system with single antenna mobile terminals (MTs) where channel state information at the base station is provided through limited uplink feedback (FB). In order to reduce the FB rate and signal processing complexity, the available bandwidth is divided into resource blocks (RBs) whose number of subcarriers reflects the coherence bandwidth of the channel. This approach is very common in the standardization of 4th generation wireless communication systems and justifies an independent channel quantization per RB. Within this framework the paper contains two main contributions. Firstly we provide joint conditions on the channel coherence bandwidth and the FB rate per RB that allow for a simpler quantization of the RB channel matrix (space-frequency) by a space vector, causing negligible performance loss in terms of system achievable throughput. This is accomplished after deriving a new metric for codebook design in RB channel quantization that exploits spatial and frequency correlation. As a second contribution we investigate the trade-off between accurate channel knowledge and frequency/multiuser diversity. It is seen that even for a moderate number of MTs in the network, concentrating all the available FB bits in characterizing only one RB provides a significant gain in system throughput over a more classical distributed approach and this result is validated both analytically and by simulations.

Single-user, multiuser, and network MIMO performance is evaluated for downlink cellular networks with 12 antennas per site, sectorization, universal frequency reuse, scheduled packet-data, and a dense population of stationary users. Compared to a single-user MIMO baseline system with 3 sectors per site, network MIMO coordination is found to increase throughput by a factor of 1.8 with intra-site coordination among antennas belonging to the same cell site. Intra-site coordination performs almost as well as a highly sectorized system with 12 sectors per site. Increasing the coordination cluster size from 1 to 7 sites increases the throughput gain factor to 2.5.

We consider a multiuser MIMO-OFDM downlink system with single antenna mobile terminals (MTs) where channel state information at the base station is provided through limited uplink feedback (FB). In order to reduce the FB rate and signal processing complexity, the available bandwidth is divided into resource blocks (RBs) whose number of subcarriers reflects the coherence bandwidth of the channel. This approach is very common in the standardization of 4th generation wireless communication systems and justifies an independent channel quantization per RB. The paper has two main contributions: firstly we show conditions on the coherence bandwidth of the channel and the FB rate per RB that allow for a simpler characterization of the RB channel matrix by a space vector, causing negligible performance loss. This is accomplished after deriving a new performance metric for RB channel quantization that exploits spatial and frequency correlation. As a second contribution we investigate the trade-off between accurate channel knowledge and frequency/multiuser diversity. It is seen that even for a moderate number of MTs in the network, concentrating all the available FB bits in characterizing only one RB provides a significant gain in system throughput over a more classical distributed approach and this result is validated both analytically and by simulations.

We consider a multiuser MIMO downlink system with multiple antenna mobile terminals where channel state information at the base station is provided through limited uplink feedback. In a multi-cell environment we compare two transceiver architectures based on zero forcing and unitary beamforming respectively. For both schemes we show how the additional degrees of freedom provided by multiple receive antennas can be exploited to improve system performance, by a proper design of the receive combiner. We propose channel quantization solutions that adapt to the transceiver architectures and exploit MIMO channel statistic. Numerical simulations validate the effectiveness of the proposals in a multi-cell environment where long-term proportional fairness is guaranteed among the users in the network.

We consider a MIMO broadcast channel where both the transmitter and receivers are equipped with multiple antennas. Channel state information at the transmitter (CSIT) is obtained through limited (i.e., finite-bandwidth) feedback from the receivers that index a set of precoding vectors contained in a predefined codebook. We propose a novel transceiver architecture based on zero-forcing beamforming and linear receiver combining. The receiver combining and quantization for CSIT feedback are jointly designed in order to maximize the expected SINR for each user. We provide an analytic characterization of the achievable throughput in the case of many users and show how additional receive antennas or higher multiuser diversity can reduce the required feedback rate to achieve a target throughput.We also propose a design methodology for generating codebooks tailored for arbitrary spatial correlation statistics. The resulting codebooks have a tree structure that can be utilized in time-correlated MIMO channels to significantly reduce feedback overhead. Simulation results show the effectiveness of the overall transceiver design strategy and codebook design methodology compared to prior techniques in a variety of correlation environments.

For a cellular system based on frequency division duplexing where the base station (BS) is equipped with multiple antennas and the mobile terminals (MTs) have one antenna each, we propose joint techniques to a) feed back channel state information from MTs to the BS, b) design the beamformer and c) schedule downlink transmissions. We propose that both BS and MT predict channel variations, and the feedback (FB) information from MT to BS is given by the prediction error. For the beamformer design we both consider a zero forcing approach and investigate a new solution based on the minimum mean square error criterion, which takes into account the quantization error. By exploiting the FB information, we derive approximated expressions of the signal to noise ratio relative to each MT, used at BS to perform scheduling with an efficient greedy algorithm. Performance assessment on realistic 3GPP channel models show that the proposed techniques provide a significant improvement of the network throughput at a lower FB rate than existing solutions.

We propose a user selection algorithm and the supporting feedback scheme, for MIMO Gaussian broadcast channels with arbitrary number of users, transmit beamforming and limited channel state information (CSI) available at the transmitter. The method relies on estimation of the user signal-to-interference-plus-noise ratios (SINR's) at the transmitter, by means of an appropriate finite-rate channel report from the mobiles. The basic principle is that users are added successively only if they are beneficial to the system in terms of increased throughput. Numerical results show improved performance compared to other finite-rate feedback schemes.