In wireless networks, communication links may be subject to random fatal impacts: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably the information can be collected over attacked links. For a single point-to-point channel subject to a random attack, named as a dying channel, we model it as a block-fading (BF) channel with a finite and random channel length. First, we study the outage probability when the coding length K is fixed and uniform power allocation is assumed. Furthermore, we discuss the optimization over K and the power allocation vector PK to minimize the outage probability. In addition, we extend the single point to-point dying channel case to the parallel multi-channel case where each sub-channel is a dying channel, and investigate the corresponding asymptotic behavior of the overall outage probability with two different attack models: the independent-attack case and the m-dependent-attack case. It can be shown that the overall outage probability diminishes to zero for both cases as the number of sub-channels increases if the rate per unit cost is less than a certain threshold. The outage exponents are also studied to reveal how fast the outage probability improves over the number of sub-channels. Besides the information-theoretical results, we also study a practical coding scheme for the dying binary erasure channel (DBEC), which is a binary erasure channel (BEC) subject to a random fatal failure. We consider the rateless codes and optimize the degree distribution to maximize the average recovery probability. In particular, we first study the upper bound of the average recovery probability, based on which we define the objective function as the gap between the upper bound and the average recovery probability achieved by a particular degree distribution. We then seek the optimal degree distribution by minimizing the objective function. A simple and heuristic approach is also proposed to provide a suboptimal but good degree distribution.

This thesis concerns communication across channels with multiple inputs and multiple outputs. Specifically, we consider the closed-loop scenario in which knowledge of the state of the multiple-input multiple-output (MIMO) channel is available at the transmitter. We show how this knowledge can be exploited to optimize performance, as measured by the zero-outage capacity, which is the capacity corresponding to zero outage probability. On at-fading channels, a closed-loop transmitter allocates different powers and rates to the multiple channel inputs so as to maximize zero-outage capacity. Frequency-selective fading channels call for a combination of orthogonal-frequency-division multiplexing (OFDM) and MIMO known as MIMO-OFDM. This exacerbates the allocation problem because it multiplies the number of allocation dimensions by the number of OFDM tones. Fortunately, this thesis demonstrates that simple allocations are sufficient to approach the zero-outage capacity. These simple strategies exploit the tendency for random MIMO channels to behave deterministically as the number of inputs becomes large.

The reliability and data rate of wireless communication have traditionally been limited by the presence of multipath fading in wireless channels. However, dramatic performance improvements can be obtained by the use of multiple transmit and receive antennas. Specifically, multiple antennas increase reliability by providing diversity gain, namely greater immunity to deep channel fades. They also increase data rates by providing multiplexing gain, i.e., the ability to multiplex multiple symbols in one signaling interval. Harvesting the potential benefits of multiple antennas requires the use of specially designed space-time codes at the transmitter front-end. Space-time codes introduce redundancy in the transmitted signal across two dimensions, namely multiple transmit antennas and multiple signaling intervals. In this work, we focus on linear space-time codes, which linearly combine the real and imaginary parts of their complex inputs to obtain transmit vectors for multiple signaling intervals. We aim to design optimum linear space-time codes. Optimality metrics and design principles for space-time codes are shown to depend strongly on the codes' function in the overall transmitter architecture. We consider two cases, depending on whether or not the space-time code is complemented by a powerful outer error-control code. In the absence of an outer code, the multiplexing gain of a space-time code is measured by its rate, while its diversity gain is measured by its raw diversity order. To maximize multiplexing and diversity gains, the space-time code must have maximum possible rate and raw diversity order. We show that there is an infinite set of maximum-rate codes, almost all of which also have maximum raw diversity order. However, different codes in this set have different error rate for a given input alphabet and SNR. Therefore, we develop analytical and numerical optimization techniques to find the code in this set which has the minimum union bound on error rate. Simulation results indicate that optimized codes yield significantly lower error rates than unoptimized codes, at the same data rate and SNR. In a concatenated architecture, a powerful outer code introduces redundancy in the space-time code inputs, obtaining additional diversity. Thus, the raw diversity order of the space-time inner code is only a lower limit to the total diversity order of the concatenated transmitter. On the other hand, we show that the rate of the space-time code places an upper limit on the multiplexing ability of the concatenated architecture. We conclude that space-time inner codes should have maximum possible rate but need not have high raw diversity order. In particular, the serial-to-parallel converter, which introduces no redundancy at all, is a near-optimum space-time inner code. This claim is supported by simulation results. On the receiver side, we generalize the well known sphere decoder to develop new detection algorithms for stand-alone space-time codes. These new algorithms are extended to obtain efficient soft-output decoding algorithms for space-time inner codes.

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