Channels with state model communication settings where the channel statistics are not fully known or vary over transmissions. It is important for a communication system to obtain the channel state information in terms of increasing channel capacity. This thesis addresses the effect of the quality of state information on channel capacity. Extreme scenarios are studied to reveal the limit in increasing channel capacity with the knowledge of state information. We consider the channel with the perfect state information at the decoder, while the encoder is only available to a noisy state observation. The effect of the noisy state at the encoder to the channel capacity is studied. We show that for any binary-input channel if the mutual information between the noisy state observation at the encoder and the true channel state is below a positive threshold determined solely by the state distribution, then the capacity is the same as that with no encoder side information. A complementary phenomenon is also revealed for the generalized probing capacity. Extensions beyond binary-input channels are developed. We further investigate the channel capacity, when the causal channel state information (available at the encoder or the decoder or both) makes it deterministic. Every such a capacity is called an intrinsic capacity of the channel. Among them, the smallest and the largest called the lower and the upper intrinsic capacities, are particularly studied. Their exact values are determined in most cases when the input or the output is binary. General lower and upper bounds are also provided for the lower and the upper intrinsic capacities with causal state information available at both sides. Byproducts of this work are a generalization of the Birkhoff-von Neumann theorem and a result on the uselessness of causal state information at the encoder. / Thesis / Doctor of Philosophy (PhD) / It is well known that with the knowledge of channel state, it is possible to increase the channel capacity. In this sense, knowing channel state never hurts. However, whether it is always bene cial to actively acquire channel state is another story. If we take into account the cost of measuring the channel state against the potential gain on the capacity, sometimes it may not appear very economic to do so. This thesis studies the effect of the quality of observed channel states on the channel capacity. It has been found out in some circumstances the channel capacity is very sensitive to the noise on the state information. On the other hand, it appears that the maximum capacity can be achieved with the knowledge of a small portion of the total channel state information under a slightly different setting. This thesis proves the generality of such phenomena in binary-input channels and provides the necessary and sufficient conditions for the occurrence of such phenomena for an arbitrary channel. This paper also introduces the idea of intrinsic capacity which can be used to measure the ultimate capacity potential of a channel by exploring the channel state. By viewing an arbitrary channel as a deterministic channel with state, the greatest possible and smallest possible capacities have been either derived or bounded in the thesis.

Cellular systems are interference limited in nature. This problem is further accentuated in upcoming commercial wireless standards, which intend to use all the available spectrum in every cell in the network to improve peak data rates. This, however, could lead to considerable interference among neighboring cells, decreasing data rates and causing outages at the cell-edge. Multicell cooperation offers a solution for reducing the high levels of interference. The basic idea is that base stations coordinate transmissions by sharing user information among themselves via backhaul links. With the backhaul being bandwidth limited, cooperative strategies that involve the exchange of only user channel state information (CSI) among base stations offer the best tradeoff between complexity, backhaul load and performance gains. This dissertation focuses on these partial cooperative techniques, known as coordinated beamforming in 3GPP LTE Advanced. In existing frequency division duplex systems, users estimate and feedback the CSI of a single channel over a finite-bandwidth feedback link, using limited feedback techniques. In a multicell cooperative scenario, each user needs to transmit the CSI of multiple channels using the same feedback link. This implies that the available feedback bandwidth must be efficiently shared among different channels to maximize performance gains in the cellular network. This dissertation develops three different approaches to limited feedback in multicell cooperative systems. The first technique, separate quantization, involves each channel being fed back individually using a different codebook. Closed-form expressions are derived to partition adaptively the available feedback bits, as a function of the signal strengths and delays associated with each of the multiple channels. The second strategy is known as joint quantization, where the CSI of all the channels are quantized together as a composite vector. It is shown that though this approach yields higher data rates with smaller feedback requirements than separate quantization, it requires the design and storage of special codebooks. Finally, predictive joint quantization is proposed to exploit the temporal correlation of the wireless channel to reduce feedback requirements significantly as compared to the other two strategies, at the cost of high complexity at the user terminals. / text

This thesis considers a novel problem of obtaining global channel state information (CSI) at every node in an ad-hoc wireless network. A class of protocols for dissemination and estimation are developed which attempt to minimize the staleness of the estimates throughout the network. This thesis also provides an optimal protocol for CSI dissemination in networks with complete graph topology and a near optimal protocol in networks having incomplete graph topology. In networks with complete graph topology, the protocol for CSI dissemination is shown to have a resemblance to finding Eulerian tours in complete graphs. For networks having incomplete graph topology, a lower bound on maximum staleness is given and a near optimal algorithm based on finding minimum connected dominating sets and proper scheduling is described in this thesis.

This thesis considers a novel problem of obtaining global channel state information (CSI) at every node in an ad-hoc wireless network. A class of protocols for dissemination and estimation are developed which attempt to minimize the staleness of the estimates throughout the network. This thesis also provides an optimal protocol for CSI dissemination in networks with complete graph topology and a near optimal protocol in networks having incomplete graph topology. In networks with complete graph topology, the protocol for CSI dissemination is shown to have a resemblance to finding Eulerian tours in complete graphs. For networks having incomplete graph topology, a lower bound on maximum staleness is given and a near optimal algorithm based on finding minimum connected dominating sets and proper scheduling is described in this thesis.

This thesis considers a novel problem of obtaining global channel state information (CSI) at every node in an ad-hoc wireless network. A class of protocols for dissemination and estimation are developed which attempt to minimize the staleness of the estimates throughout the network. This thesis also provides an optimal protocol for CSI dissemination in networks with complete graph topology and a near optimal protocol in networks having incomplete graph topology. In networks with complete graph topology, the protocol for CSI dissemination is shown to have a resemblance to finding Eulerian tours in complete graphs. For networks having incomplete graph topology, a lower bound on maximum staleness is given and a near optimal algorithm based on finding minimum connected dominating sets and proper scheduling is described in this thesis.

Cooperative systems exploit spatial diversity to improve communication quality. But system performances could be severely degraded in the existance malicious relay nodes. In this thesis, we consider a two-relay decode-and-forward (DF) cooperative network. Relay nodes adopt Orthogonal Space Time Block Code (OSTBC) to achieve spatial diversity. Assume that relay nodes may misbehave with a certain probability. If a relay is malicious, it will garble transmission signals, resulting in severe damage to system performance. In the literature, detecting malicious relays requires perfect channel state information. However, misbehavior of the relay will first lead to inaccurate channel estimation. Therefore, we propose a novel detecting misbehavior scheme to deal with the dilemma. Since misbehavior of relays influences statistical properties of the estimated channel coefficients, destination can detect misbehaving by comparing the distribution of channel estimates. The most important of all is that we don¡¦t need channel state information to enhance detecting performance. Through simulation results, we verify proposed scheme can detect misbehavior effectively without channel state information. Compared with signal-to-noise ratio, the number of received tracing symbols has more significant impact on detecting misbehavior of the relay.

This paper presents the investigations into the performance of a multiple-input multiple-output (MIMO) system with its transmitters on a spinning vehicle and no available channel state information (CSI) at the transmitter or the receiver. The linear least squares approach is used to estimate the channel and the estimation error is measured. Spinning gives rise to a periodic component in the channel which can be estimated based on the spin rate relative to the data rate of the system. It is also determined that spinning causes the bit error rate of the system to degrade by a few dB.

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.

Empowered by linear precoding and decoding, a spatially multiplexed multiple-input multiple-output (MIMO) system becomes a convenient framework to offer high data rate, diversity and interference management. While most of the current precoding/decoding designs have assumed perfect channel state information (CSI) at the receiver, and sometimes even at the transmitter, in this thesis we design the precoder and decoder with imperfect CSI at both the transmit and the receive sides, and investigate the joint impact of channel estimation errors and channel correlation on system structure and performance. The mean-square error (MSE) related performance metrics are used as the design criteria. We begin with the minimum total MSE precoding/decoding design for a single-user MIMO system assuming imperfect CSI at both ends. Here the CSI includes the channel estimate and channel correlation information. The structures of the optimum precoder and decoder are determined. Compared to the perfect CSI case, linear filters are added to the transceiver structure to improve system robustness against imperfect CSI. The effects of channel estimation error and channel correlation are quantified by simulations. With imperfect CSI at both ends, the exact capacity expression for a single-user MIMO channel is difficult to obtain. Instead, a tight capacity lower-bound is used for system design. The optimum structure of the transmit covariance matrix for the lower-bound has not been found in the existing literature. By transforming the transmitter design into a joint precoding/decoding design problem, we derive the expression of the optimum transmit covariance matrix. The close relationship between the maximum mutual information design and the minimum total MSE design is also discovered assuming imperfect CSI. For robust multiuser MIMO communications, minimum average sum MSE transceiver (precoder-decoder pairs) design problems are formulated for both the uplink and the downlink, assuming imperfect channel estimation and channel correlation at the base station (BS). We propose improved iterative algorithms based on the associated Karush-Kuhn-Tucker (KKT) conditions. Under the assumption of imperfect CSI, an uplink--downlink duality in average sum MSE is proved. As an alternative for the uplink optimization, a sequential semidefinite programming (SDP) method is proposed. Simulation results are provided to corroborate the analysis. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-07-25 10:53:45.175

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a non-cooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is proved that the stationary optimal transmission scheduling policy is a randomized mixture of two policies that are threshold in the buffer occupancy state. A stochastic approximation algorithm and a Q-learning based algorithm that can adaptively estimate the optimal transmission scheduling policies are then proposed.