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The aim of this manuscript is to propose and compare several distributed linear precoding schemes for the downlink of multicell MISO-OFDM based systems. The precoder vectors are computed in a distributed manner at each BS, assuming that the BSs have only knowledge of local channel state information and considering several criteria: distributed zero-forcing (DZF), maximum ratio transmission and distributed virtual signal-to-interference noise ratio (DVSINR). Then the system is further optimized through centralized power allocation. Two centralized power allocation algorithms with per-BS power constraint and different complexity tradeoffs are proposed: namely, minimization of the instantaneous average BER and minimization of the sum of inverse of signal-to-interference noise ratio, for which a closed-form power allocation scheme is derived. The performance of the proposed schemes is compared considering typical pedestrian scenarios based on LTE specifications. The numerical results show that increasing the number of degrees of freedom or for high SNR, the proposed DZF approaches tends to the DVSINR based ones.

The rapid development of wireless communications is not possible without advancement of the wireless transceiver technology. Regardless the wide area network (WAN) or local area network (LAN), wireless transceivers constitute the physical layer of any wireless network. Notwithstanding the hardware progress, algorithms for digital signal processing in wireless transceivers have also experienced major developments in the past two decades. Notables include those for MIMO wireless channels in order to increase the data rate and to satisfy the more stringent quality of service (QoS) requirements. This chapter focuses on signal processing side of the wireless transceiver technology, including channel estimation, equalization, precoding and data or symbol detection which are fundamental to design of wireless transceivers.In this chapter, several wireless systems will be introduced, with emphasis on direct sequence (DS) spread sequence CDMA (code division multiple access) systems and OFDM (orthogonal frequency division multiplex) modulated systems over fading channels. Optimal algorithms will be presented for zero-forcing channel equalization, precoding, and linear receivers. These algorithms are developed for MIMO channels due to the multiuser nature of the wireless communication system and multiple antenna technology. Finally optimal multiuser symbol detection will be considered jointly with decision feedback. It will be seen that Kalman filtering and LQR control play pivotal roles in developing optimal design algorithms for wireless transceivers.

In this chapter we study the single-user MIMO channel which is used to model the communication link between a base station and a user. We explore in more detail the fundamental results that were briefly described in the previous chapter. We derive the open-loop and closed-loop MIMO channel capacities and describe techniques for achieving capacity including architectures known as V-BLAST and D-BLAST. We also describe classes of suboptimal techniques such as linear receivers, space-time coding for transmitting a single data stream from multiple antennas, and precoding when there is limited knowledge of CSI at the transmitter.

As described in the previous chapters, multiple-input multiple-output (MIMO) wireless communication systems are able to provide large gains in capacity and link quality by capitalizing either on spatial multiplexing [7, 8, 11, 12, 40] or on space–time coding [1, 39] techniques or, yet, on hybrid combinations of both [6, 14, 44]. Conventional spatial multiplexing and transmit diversity techniques are referred to as “open-loop” since they do not rely on any form of channel information at the transmitter. Despite their practical importance, open-loop multiple-antenna techniques are very sensitive to the ill-conditioning (i.e., rank deficiency) of the channel matrix which incurs limited diversity gains and increased probability of error. Performance enhancements of MIMO systems in terms of higher spectral efficiencies and lower error rates can be achieved by using “closed-loop” transmission techniques [31]. In contrast to the open-loop schemes, these techniques exploit the knowledge of the channel conditions at the transmitter for adapting the transmission signal aiming at improving the link performance. Multiple-antenna techniques like transmit beamforming or precoding [20] work traditionally in a closed-loop mode. The set of all closed-loop techniques is also called herein channel-adaptive MIMO. They are mandatory in upcoming wireless standards for achieving the challenging requirements of beyond 3G systems like 3rd. Generation Partnership Project (3GPP) long-term evolution (LTE). In the actual wireless communication system, when closed-loop transmission is implemented, some information is sent back to the transmitter from the receiver through a feedback control channel. This channel is usually shared among all users, being modeled as a limited-rate channel. The design of such channels and the quantization of the information to be reported back through them is defined as a feedback problem. In the past few years, this problem has gained increased attention due to the importance of considering channel-adaptive MIMO transmission. A first approach

This book provides a comprehensive review of precoding techniques for digital communications systems from a signal processing perspective. In the first part of the book the authors provide an overview of the principles of precoding for channels with intersymbol interference (ISI) such as Tomlinson-Harashima precoding and Trellis precoding. There is also a discussion on how the widely used OFDM systems can be treated as a special case of precoding techniques and introduce precoding schemes for OFDM systems. Further, the book provides an overview of various existing precoding techniques that reduce the interference level while keeping the receiver design simple in CDMA systems. There is also a complete chapter on the issue of precoding for multiple input multiple output (MIMO) channels. The second part of the book introduces the state-of-the-art precoding techniques originating from several projects and research activities involving multiuser OFDM transmissions and ultra-wideband (UWB) radio systems.