**1. Introduction**

[69] Cimini Jr L. J. and Sollenberger N. R. Peak-to-average power ratio reduction of an ofdm signal using partial transmit sequences. IEEE Communications Letters 2000;4

[70] Tellambura C. Improved phase factor computation for the par reduction of an ofdm

[71] Han S. H. and Lee J. H. Papr reduction of ofdm signals using a reduced complexity

[72] Wen J. H., Lee S. H., Huang Y. F. and Hung H. L. A suboptimal pts algorithm based on paticle swarm optimization for papr reduction in ofdm systems. EURASIP J.

[73] Wang X. H., Wang B. Z. and Chen K. J. Compact broadband dual-band bandpass fil‐ ters using slotted ground structures. Progress in Electromagnetics Research 2008;82

[74] Chen C. Y. and Hsu C. Y. A simple and effective method for microstrip dual-band filters design. IEEE Microwave and Wireless Components Letters 2006;16 (5) 246-248.

[75] Koziel S. and Bandler J. W. Space mapping with multiple coarse models for optimi‐ zation of microwave components. IEEE Microwave and Wireless Components Let‐

signal using pts. IEEE Communications Letters 2001;5 (4) 135-137.

pts technique. IEEE Signal Processing Letters 2004;11 (11) 887-890.

Wireless Commun. Networking 2008;2008 (14)

(3) 86-88.

24 Contemporary Issues in Wireless Communications

(151-166.

ters 2008;18 (1) 1-3.

For broadcast channels (BC) in multiuser multiple-input multiple-output (MU-MIMO) systems, the overall achievable performance depends on the efficient use of the resources at the base station (BS). When channel state information (CSI) is known at the transmitter the gain is twofold: full spatial degrees of freedom can be attained [1] and the system can be optimized over a new degree of freedom given by the users. In the literature of MU-MIMO systems two dimensions of optimization are studied: *multiplexing diversity* and *multiuser diversity* (MUD). The former is a consequence of the independent fading across all MIMO links, which yields a set of parallel spatial channels where different data streams can be transmitted increasing the system capacity [2]. The latter is given when users that are geographically far apart have channels that fade independently at any point in time. For the system sum rate maximization, such independent fading process is exploited so that the user with the largest channel magnitude will be selected for transmission. In MU-MIMO systems spatial and temporal communication can be performed in two ways: 1) orthogonal multiple access (time, frequency, or code) and 2) spatial-division multiple access (SDMA). To achieve orthogonality in time, all the spatial resources at the BS (*Nt* antennas) are used to communicate with one user (*Nr* antennas) at a time, which is known also as time-division multiple access (TDMA). This technique avoids inter-user interference, achieves power gains that scale with *Nt*, enhances data rates for a single user specially at low signal-to-noise ratio (SNR) regime, and is robust to CSI uncertainty [1], [3]. SDMA exploits CSI at the BS allowing *K* >1 users to be scheduled at the same time achieving multiplexing gains of min(*Nt*, *K Nr*) at high SNR regime, where the system is limited by the degrees of freedom and not by power. Since the BS has knowledge of the data symbols and CSI, the multiuser transmission can be optimized using coding techniques. The achievable rate region in BC is based on dirty paper coding (DPC) [4].

© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The principle behind this optimum coding technique is that the BS knows the interference for a given user and can pre-subtract it before transmission, which yields the capacity of an interference free channel. However, DPC is a nonlinear process that requires successive encoding and decoding whose complexity is prohibitive in practical systems and other suboptimal techniques are preferred instead. In the literature, DPC has been interpreted as *beamforming* (BF) [5] which is a SDMA scheme where data streams of different users are encoded independently and multiplied by weight vectors in order to mitigate mutual inter‐ ference. Although BF is a suboptimal multiuser transmission scheme, several works (e.g., [3], [5]) have shown that it can achieve a large portion of the DPC rate and its performance is closeto-optimal for large *Nt* and *K*. Nevertheless, the optimization of the downlink BF weight vectors is a non-convex problem [6]. The specific selection of the weight vector of a given user may affect the performance of other users, i.e., the achievable signal-to-interference-plus-noise ratio (SINR) of one user is coupled with the other users weight vectors and transmit powers [6]. Since the weight vectors must be optimized jointly, optimum BF is a complicated task and suboptimal weights given by Zero-Forcing (ZF) based methods can be used [5]. The joint optimization of the beamforming weights, the power allocation, and the set of links that are scheduled under SDMA mode is performed for a given global objective function. In BC system literature a meaningful figure of merit is the total sum rate since it quantifies the total data flow for a specific BF scheme and the open problems are power allocation and user selection. If the set of scheduled users meets that *Nt* ≥ *K Nr*, power allocation can be given either by Lagrangian methods (convex optimization [7]) or by equal power allocation (close-to-optimal for the high SNR regime [8]–[12]). However, when the set of users is larger than the number of spatial resources at the BS, i.e., *Nt* < *K Nr*, user selection is required. The selection of the optimum set of users that maximizes the sum rate for a given BF scheme with optimum power allocation is a mixed non-convex problem. Indeed, in systems where users must be allocated in different radio resources (slots or sub-channels) finding the optimum subsets of users under SDMA mode for each radio resource is a NP-complete problem whose optimal solution can only be found via exhaustive search (ES) [13]. Recent research works have proposed a number of feasible heuristics algorithms that find a suboptimal yet acceptable solution to the sum rate maximization problem with SDMA communication. The literature of MU-MIMO has been focused on ZF-based BF schemes due to their tractability and the fact that some properties of the wireless channels can be used to estimate the reliability of joint transmission for a given set of users. The main objective of joint scheduling and BF is to make better decisions at the media access control (MAC) layer by exploiting information from the physical (PHY) layer. The literature reviewed in this chapter addresses the scheduling (user selection) process in the MAC layer using PHY layer information without considering constraints from upper layers. Our aim is to provide a comprehensive overview of algorithms proposed over the last years regarding joint user selection and SDMA schemes that solve the sum rate maximization problem in MU-MISO BC systems.
