Channel Shortening in Wireless Communication

The concept of Channel Shortening (CS) is a well-known technique that has a rich history over 40 years. CS transfers linear vector channels such as multi-input multi-output (MIMO) and intersymbol interference (ISI) channels into ``shortened'' versions, for the purpose of reducing demodulation-complexity and improving data-transmission performance. The original CS idea can trace back to the minimum-phase filtering on ISI channels to concentrate channel energy into a first few number of channel taps, and afterwards truncate the remaining channel tails or mitigate the ISI according to the tails with fed back hard-decisions. Since then, many other CS techniques have been extensively developed through various criteria. The CS demodulators investigated in this thesis are based on maximizing the achievable information rate (AIR), which is also referred to as the generalized mutual information (GMI).This thesis comprises three parts, with AIR-maximization based CS extensively investigated both for reduced-complexity demodulation and precoding designs for wireless communication systems, and then followed by investigations on a newly envisioned system that is beyond traditional massive MIMO. In the first part, the designs of CS demodulators are considered for turbo equalization in linear vector channels with priori informations from outer decoder. Following that, a low-complexity reduced-state soft-output Viterbi equalizer (RS-SOVE) for ISI channels in a non-iterative receiver structure, and an AIR based partial marginalization (AIR-PM) detector for MIMO channels are introduced, respectively. In addition, a novel modulus operation based MIMO detection, namely, the modulus zero-forcing (MZF) detector, is proposed for boosting the detection performance of linear equalizers. In the second part, the CS idea is extended to precoder designs, and a generalized zero-forcing based dirty-paper (GZF-DP) precoder is developed for the broadcast channel (BC). Later, a linear precoder design is considered for MIMO-ISI channel, with priori information that receivers are using CS demodulation. In the last part, a new concept called "Large Intelligent Surface (LIS)" beyond a traditional massive MIMO concept is envisioned and its information-theoretical properties for both data-transmission and terminal-positioning are studied. LIS in its fundamental form uses the entire surface for transmission and reception of radiating signals, which provides ultimate limits that a traditional large antenna-array system can possible achieve within the same deployed surface-area. In addition, as the effective channel after a matched-filtering (MF) process can be modeled as a sinc-function like linear vector channel, the CS techniques developed in the previous two parts can also be applied to the LIS system. (Less)