We consider the problem of transmitting data at rate over a state-dependent channel with state information available at the sender and at the same time conveying the information about the channel state itself to the receiver. The amount of state information that can be learned at the receiver is captured by the mutual information between the state sequence and the channel output . The optimal tradeoff is characterized between the information transmission rate and the state uncertainty reduction rate , when the state information is either causally or noncausally available at the sender. In particular, when state transmission is the only goal, the maximum uncertainty reduction rate is given by . This result is closely related and in a sense dual to a recent study by Merhav and Shamai, which solves the problem of masking the state information from the receiver rather than conveying it.

We consider the problem of transmitting data at rateRover a state dependent channelp(y\x,s)with the state information available at the sender and at the same time conveying the information about the channel state itself to the receiver. The amount of state information that can be learned at the receiver is captured by the mutual information I(Sn;Yn) between the state sequence Snand the channel output Yn. The optimal tradeoff is characterized between the information transmission rateRand the state uncertainty reduction rate Delta, when the state information is either causally or noncausally available at the sender. This result is closely related and in a sense dual to a recent study by Merhav and Shamai, which solves the problem of masking the state information from the receiver rather than conveying it.

We formulate a problem of state information transmission over a state-dependent channel with states known at the transmitter. In particular, we solve a problem of minimizing the mean-squared channel state estimation error E/spl par/S/sup n/ - S/spl circ//sup n//spl par/ for a state-dependent additive Gaussian channel Y/sup n/ = X/sup n/ + S/sup n/ + Z/sup n/ with an independent and identically distributed (i.i.d.) Gaussian state sequence S/sup n/ = (S/sub 1/, ..., S/sub n/) known at the transmitter and an unknown i.i.d. additive Gaussian noise Z/sup n/. We show that a simple technique of direct state amplification (i.e., X/sup n/ = /spl alpha/S/sup n/), where the transmitter uses its entire power budget to amplify the channel state, yields the minimum mean-squared state estimation error. This same channel can also be used to send additional independent information at the expense of a higher channel state estimation error. We characterize the optimal tradeoff between the rate R of the independent information that can be reliably transmitted and the mean-squared state estimation error D. We show that any optimal (R, D) tradeoff pair can be achieved via a simple power-sharing technique, whereby the transmitter power is appropriately allocated between pure information transmission and state amplification.

Writing on dirty paper, a variation of the standard additive white Gaussian noise (AWGN) channel with the channel output is considered. The Gaussian noise noncausally known at the transmitter does not affect the capacity of the AWGN channel. This is known as WDP property. The WDP property for three Gaussian multiple user channels with known capacity region is established. The achievable rate regions of corresponding discrete channels is proved. Although those regions may be suboptimal in general, they turn out to be optimal for these Gaussian channels. Alternative proofs can be obtained by successive uses of Costa's WDP coding scheme, from which the WDP property can be established for more general noise and state distributions.

We present an efficient method to optimize network resource allocations under nonlinear quality of service (QoS) constraints. We first propose a suite of generalized proportional allocation schemes that can be obtained by minimizing the information-theoretic function of relative entropy. We then optimize over the allocation parameters, which are usually design variables an engineer can directly vary, either for a particular user or for the worst-case user, under constraints that lower bound the allocated resources for all other users. Despite the nonlinearity in the objective and constraints, we show that this suite of resource allocation optimization can be efficiently solved for global optimality through a convex optimization technique called geometric programming. This general method and its extensions are applicable to a wide array of resource allocation problems, including processor sharing, congestion control, admission control, and wireless network power control. We provide a specific example of efficiently optimizing an admission control scheme.

We present a systematic treatment of efficient nonlinear optimizations of queuing systems. The suite of formulations uses the computational tool of convex optimization, with fast polynomial time algorithms to obtain the global optimum for these nonlinear problems under various constraints. We first show convexity structures of several queuing systems, including some surprising transition patterns, followed by formulating and showing numerical examples of several convex performance optimizations for both single queues and queuing networks. Blocking probability minimization and service rate allocation through the effective bandwidth approach is also presented.

Using multiple antennas at both the transmitter and receiver, BLAST exploits the multipath characteristics of a wireless channel to enable very large capacities. In an extended V-BLAST, those capacities are approached with single-dimensional coding and rate adaptation. However, such results require infinite precision for rate and power settings and perfect channel estimation. These assumptions are not practical due to a limited data rate of the feedback channel and a rapidly changing nature of a wireless channel. An algorithm for discretizing the rates and power control levels is proposed. We show that the loss in spectral efficiency due to rate and power control discretization is about 1-2 dB compared to that of an eigenmode transmission. We also consider the effects of imperfect channel estimation. We show that the performance loss is minimal when the channel estimation error is less than 2-3%.