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We consider the problem of controlling a linear time invariant process when the controller is located at a location remote from where the sensor measurements are being generated. The communication from the sensor to the controller is supported by a communication network with arbitrary topology composed of analog erasure channels. Using a separation principle, we prove that the optimal linear-quadratic-Gaussian (LQG) controller consists of an LQ optimal regulator along with an estimator that estimates the state of the process across the communication network. We then determine the optimal information processing strategy that should be followed by each node in the network so that the estimator is able to compute the best possible estimate in the minimum mean squared error sense. The algorithm is optimal for any packet-dropping process and at every time step, even though it is recursive and hence requires a constant amount of memory, processing and transmission at every node in the network per time step. For the case when the packet drop processes are memoryless and independent across links, we analyze the stability properties and the performance of the closed loop system. The algorithm is an attempt to escape the viewpoint of treating a network of communication links as a single end-to-end link with the probability of successful transmission determined by some measure of the reliability of the network.

We consider the problem of differentiated rate scheduling for the downlink (i.e., multi-antenna broadcast channel), in the sense that the rates required by different users must satisfy certain constraints on their ratios. When full channel state information (CSI) is available at the transmitter and receivers, the problem can be readily solved using dirty paper coding (DPC) and the application of convex optimization techniques on the dual problem which is the multiple access channel (MAC). Since in many practical application full CSI may not be feasible and computational complexity prohibitive when the number of users is large, we focus on other simple schemes that require very little CSI: time-division opportunistic (TO) beamforming where in different time slots (of different lengths) the transmitter performs opportunistic beamforming to the users requiring the same rate, and weighted opportunistic (WO) beamforming where the random beams are assigned to those users having the largest weighted SINR. For single antenna systems we also look at the capacity-achieving superposition coding (SC) scheme. In all cases, we determine explicit schedules to guarantee the rate constraints and show that, in the limit of large number of users, the throughput loss compared to the unconstrained throughput (sum-rate capacity) tends to zero. We further provide bounds on the rate of convergence of the sum-rates of these schemes to the sum-rate capacity. Finally, we provide simulation results of the performance of different scheduling schemes considered in the paper.

We consider the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For additive white Gaussian noise (AWGN) channels, it is well known that power efficiency is attained in the low signal-to-noise ratio (SNR) regime where capacity is proportional to the transmit power. In this paper, we first show that for a random sensory wireless network with n users (nodes) placed in a domain of fixed area, with probability converging to one as n grows, the power efficiency scales at least by a factor of /spl radic/n. In other words, each user in a wireless channel with n nodes can support the same communication rate as a single-user system, but by expending only 1//spl radic/n times the energy. Then we look at a random ad hoc network with n relay nodes and r simultaneous transmitter/receiver pairs located in a domain of fixed area. We show that as long as r/spl les//spl radic/n, we can achieve a power efficiency that scales by a factor of /spl radic/n. We also give a description of how to achieve these gains.

In this paper, a special class of wireless networks, called wireless erasure networks, is considered. In these networks, each node is connected to a set of nodes by possibly correlated erasure channels. The network model incorporates the broadcast nature of the wireless environment by requiring each node to send the same signal on all outgoing channels. However, we assume there is no interference in reception. Such models are therefore appropriate for wireless networks where all information transmission is packetized and where some mechanism for interference avoidance is already built in. This paper looks at multicast problems over these networks. The capacity under the assumption that erasure locations on all the links of the network are provided to the destinations is obtained. It turns out that the capacity region has a nice max-flow min-cut interpretation. The definition of cut-capacity in these networks incorporates the broadcast property of the wireless medium. It is further shown that linear coding at nodes in the network suffices to achieve the capacity region. Finally, the performance of different coding schemes in these networks when no side information is available to the destinations is analyzed.

The throughput of a multiple-antenna broadcast channel highly depends on the channel state information (CSI) at the transmitter side. However, due to the time variant nature of wireless channels, having perfect knowledge of the under- lying links appears to be a questionable assumption, especially when the number of users and/or antennas increases. Although it can become computationally prohibitive in practice, theoretically any point on the capacity region of a Gaussian broadcast channel is achievable using dirty paper coding (DPC) if full CSI is available. The aforementioned drawbacks of DPC have motivated the development of simpler transmission strategies that re- quire little CSI and yet can deliver a large portion of the capacity. One such scheme is opportunistic beam-forming that is shown to be able to achieve the same throughput scaling as that of DPC for the regime of large number of users. In this paper we investigate the performance of opportunistic beamforming when the perfect channel state information is not available; i.e., the channel estimation is erroneous. We will show that in order to maximize the throughput (sum rate capacity), the transmitter needs to back off the rate than what is suggested by the estimated channel state. We obtain the optimal back off and show that by using this modified opportunistic scheme, the same multiuser gain can be achieved.

This paper considers the problem of estimation over communication networks. Suppose a sensor is taking measurements of a dynamic process. However the process needs to be estimated at a remote location connected to the sensor through a network of communication links that drop packets stochastically. We provide a framework for computing the optimal performance in the sense of expected error covariance. Using this framework we characterize the dependency of the performance on the topology of the network and the packet dropping process. For independent and memoryless packet dropping processes we find the steady-state error for some classes of networks and obtain lower and upper bounds for the performance of a general network. Finally we find a necessary and sufficient condition for the stability of the estimate error covariance for general networks with spatially correlated and Markov type dropping process. This interesting condition has a max-cut interpretation.

Broadcast (or point to multipoint) communication has attracted a lot of research recently. In this paper, we consider the group broadcast channel where the users' pool is divided into groups, each of which is interested in common information. Such a situation occurs for example in digital audio and video broadcast where the users are divided into various groups according to the shows they are interested in. The paper obtains upper and lower bounds for the sum rate capacity in the large number of users regime and quantifies the effect of spatial correlation on the system capacity. The paper also studies the scaling of the system capacity when the number of users and antennas grow simultaneously. It is shown that in order to achieve a constant rate per user, the number of transmit antennas should scale at least logarithmically in the number of users.

In many problems in wireline networks, it is known that achieving capacity on each link or subnetwork is optimal for the entire network operation. In this paper, we present examples of wireless networks in which decoding and achieving capacity on certain links or subnetworks gives us lower rates than other simple schemes, like forwarding. This implies that the separation of channel and network coding that holds for many classes of wireline networks does not, in general, hold for wireless networks. Next, we consider Gaussian and erasure wireless networks where nodes are permitted only two possible operations: nodes can either decode what they receive (and then re-encode and transmit the message) or simply forward it. We present a simple greedy algorithm that returns the optimal scheme from the exponential-sized set of possible schemes. This algorithm will go over each node at most once to determine its operation, and hence, is very efficient. We also present a decentralized algorithm whose performance can approach the optimum arbitrarily closely in an iterative fashion.

In this paper, we consider a multi-user system called the group-broadcast system. In this scenario the users are divided into different groups. Users in each group are interested in a common information independent from that of other groups. Such a situation occurs for example in digital audio and video broadcast systems where the users are divided into various groups according to the shows they are interested in. The paper first obtains upper and lower bounds for the sum rate capacity. Then it looks at system capacity for the large number of users regime and fixed number of antennas. Finally, the case when the number of users and antennas grow simultaneously is studied. It is shown that in order to achieve a constant rate per user the number of transmit antennas should scale at least logarithmically in the number of users.

In this paper we consider the effect of channel estimation error on the capacity region of MIMO Gaussian broadcast channels. It is assumed that the receivers and the transmitter have (the same) estimates of the channel coefficients (i.e., the feedback channel is noiseless). We obtain an achievable rate region based on the dirty paper coding scheme. We show that this region is given by the capacity region of a dual multi-access channel with a noise covariance that depends on the transmit power. We explore this duality to give the asymptotic behavior of the sum-rate for a system with a large number of user, i.e., n rarr infin. It is shown that as long as the estimation error is of fixed (w.r.t n) variance, the sum-capacity is of order M log log n, where M is the number of antennas deployed at the transmitter. We further obtain the sum-rate loss due to the estimation error. Finally, we consider a training-based scheme for block fading MISO Gaussian broadcast channels. We find the optimum length of the training interval as well as the optimum power used for training in order to maximize the achievable sum-rate.

We consider the throughput of a MIMO Gaussian broadcast channel with two generalizing assumptions: (1) differentiated quality of service, in the sense that the rates required by different users must satisfy certain rational constraints, and (2) imperfect channel side information (CSI), where we assume that the estimate of the underlying channel has some error with known distribution. Theoretically, with full and perfect CSI in hand, dirty paper coding (DPC) can achieve any point on the capacity region, including the non-symmetrical boundary points. However, the full CSI requirement and the computational complexity of DPC motivates the development of simpler schemes which require little feedback from the users and can obtain a large portion of the capacity of the channel. In this paper we will look at the throughput and rate ratio achieved by schemes based on the idea of opportunistic beam-forming and employing a rate back-off mechanism in order to maximize the throughput in the case of imperfect CSI. We will determine the optimal scheduling parameters and will show the order optimality of these schemes in the regime of large number of users.

In this paper, we look at the capacity region of a special class of broadcast channels with multiple inputs at the transmitter and a number of receivers. The channel between an input of the transmitter and a receiver is modelled as an independent memoryless erasure channel. We assume that the signals coming from different inputs to the receiver do not interfere with each other. Also for each input, the transmitter sends the same signal through the channels outgoing from that input. This class of broadcast channels does not necessarily belong to the class of “more capable”. We will show that the capacity region of these broadcast channels is achieved by time-sharing between the receivers at each input. Finally, the implications of these results to the more general network setup are discussed.

In this paper, we consider a fading broadcast channel where users have different rate demands. In particular, we assume users are divided into M groups, each group of which requires the same rate, and where the ratio of the rates of the groups are given. The transmitter would like to maximize the throughput (sum of the rates to all users) while maintaining the rational rate constraints. In general, this problem appears to be computationally intractable since the ergodic capacity region is described as the convex hull of (an infinite) set of rates. In this paper, we therefore, focus on the asymptotic regime of many users (large n) where explicit results can be found. In particular, we propose three scheduling schemes to provide the rational rate constraints namely, weighted opportunistic (WO), time division opportunistic (TO), and superposition coding (SC). The WO scheduling is a generalization of the opportunistic scheduling in which we transmit to only the user that has the maximum weighted signal to noise ratio (SNR). In TO, each group has its own time slot in which the transmitter chooses the user with the best SNR from the corresponding group. Superposition coding is the one that achieves the capacity region. For each scheduling we give explicit scheme to guarantee the rational rate constraints. We also analyze the throughput loss due to rate constraints for different schemes. In particular, we show that the throughput loss compared to the maximum throughput (i.e., the sum rate capacity without any rate constraints) tends to zero for large n, and finally, we analyze the convergence rate of all the schemes.

We determine the capacity of a certain class of wireless erasure relay networks. We first find a suitable definition for the "cut-capacity" of erasure networks with broadcast at transmission and no interference at reception. With this definition, a maxflow mincut capacity result holds for the capacity of these networks.

We look at the power bandwidth trade-off in random sensory and ad-hoc wireless networks with n users and r/spl les/n/sup 1/2/ simultaneous source/destination pairs. Under a specific protocol, we show that the minimum power required for maintaining an achievable scaling law of R/sub sum/=/spl Theta/(f(n)) for the sum-rate in the network, scales like /spl Theta/(f(n)/n/sup 1/2/). The required bandwidth B in this case is of order /spl Theta/(f(n)/r). It is also proved that the minimum achievable energy per information bit (E/sub b//N/sub 0/)/sub min/ for this protocol, scales as /spl Theta/(1/n/sup 1/2/) and in this case the spectral efficiency is nonzero and is of constant order.

In this paper we show that "separation"-based approaches in wireless networks do not necessarily give good performance in terms of the capacity of the network. Therefore in optimal design of a wireless network, its total structure should be considered. In other words, achieving capacity on the subnetworks of a wireless network does not guarantee globally achieving capacity. We will illustrate this fact by considering some examples of multistage Gaussian wireless relay networks. We will consider a wireless Gaussian relay network with one stage in both fading and nonfading environment. We show that as the number of relay nodes, n, grows large, the capacity of this network scales like log n. We then show that with the "separation"-based scheme, in which the network is viewed as the concatenation of a broadcast and a multiaccess network, the achievable rate scales as log log n and as a constant for fading and nonfading environment, respectively, which is clearly suboptimal.

We consider the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For AWGN channels, it is well known that power efficiency is attained in the low SNR regime where capacity is proportional to the transmit power. In this paper we show that for a sensory wireless network with n nodes, or for an ad-hoc wireless network with n users and up to /spl radic/n transmit/receive pairs, the power efficiency scales at least by a factor of /spl radic/n. In other words, each user in a wireless channel with n nodes can support the same communications rate as a single user system, but by expending only 1/(/spl radic/n) the energy. We also give a description of how to achieve these gains.

We study the effect of quantization on the performance of a scalar dynamical system in the high rate regime. We evaluate the LQ cost for two commonly used quantizers: uniform and logarithmic and provide a lower bound on performance of any centroid-based quantizer based on entropy arguments. We also consider the case when the channel drops data packets stochastically.