1 An Overview on Resource Allocation Techniques for Multi-User MIMO Systems Eduardo Castaneda,˜ Member, IEEE, Adao˜ Silva, Member, IEEE, At´ılio Gameiro, and Marios Kountouris, Senior Member, IEEE Abstract—Remarkable research activities and major advances component that distributes resources among users subject to have been occurred over the past decade in multiuser multiple- individual QoS requirements; ii) a signaling strategy that input multiple-output (MU-MIMO) systems. Several transmission allows simultaneous transmission of independent data streams technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous to the scheduled users; and iii) rate allocation and power transmission of independent data streams reuse the same radio control that guarantee QoS and harness potential interference. resources. The achievable performance of such techniques heavily Fig.1 illustrates these components and the interconnection depends on the channel characteristics of the selected users, the between them. The figure highlights the fact that each function amount of channel knowledge, and how efficiently interference of the resource allocation strategy can be performed in either is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection optimal or suboptimal way, which is elaborated upon below. becomes a key approach to benefit from multiuser diversity and The multiple access schemes can be classified as orthogonal achieve full multiplexing gain. The overall performance of MU- or non-orthogonal. The former is a conventional scheme that MIMO systems is a complex joint multi-objective optimization assigns radio resources, e.g., code, sub-carrier, or time slot, problem since many variables and parameters have to be opti- to one user per transmission interval. The main characteristic mized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission of orthogonal multiple access schemes is their reliability, technique. The objective of this literature survey is to provide since there is no need to deal with co-resource interference. a comprehensive overview of the various methodologies used The resource allocation policy can optimize with reasonable to approach the aforementioned joint optimization task in the complexity several performance metrics, such as throughput, downlink of MU-MIMO communication systems. fairness, and QoS [4]. The multiplexing gain, i.e., the number Index Terms—Downlink transmission, multi-user MIMO, pre- of scheduled users, is limited by the number of available radio coding, resource allocation, spatial multiplexing, user scheduling. resources in the system. In non-orthogonal multiple access, a set of users concurrently superimpose their transmissions over the same radio resource, and potentially interfere with I. INTRODUCTION each other. In this scheme the co-resource interference can be mitigated by signal processing and transmission techniques UTURE wireless systems require fundamental and crisp implemented at the transmitter and/or receiver sides. Such F understanding of design principles and control mech- techniques exploit different resource domains, e.g., power, anisms to efficiently manage network resources. Resource code, or spatial domain, and a combination of them are allocation policies lie at the heart of wireless communication envisaged to cope with the high data rate demands and system networks, since they aim at guaranteeing the required Quality efficiency expected in the next generation of wireless networks of Service (QoS) at the user level, while ensuring efficient [5], [6]. and optimized operation at the network level to maximize Hereinafter, we focus on multiple access schemes based on arXiv:1611.04645v1 [cs.IT] 14 Nov 2016 operators’ revenue. Resource allocation management in wire- multi-antenna transceivers operating at the spatial domain, i.e., less communications may include a wide spectrum of network multiple-input multiple-output (MIMO). MIMO communica- functionalities, such as scheduling, transmission rate control, tion, where a multi-antenna base station (BS) or access point power control, bandwidth reservation, call admission control, (AP) transmits one or many data streams to one or multiple transmitter assignment, and handover [1], [2], [3]. In this user equipments simultaneously, is a key technology to pro- survey, a resource allocation policy is defined by the following vide high throughput in broadband wireless communication components: i) a multiple access technique and a scheduling systems. MIMO systems have evolved from a fundamental research concept to real-world deployment, and they have Manuscript received February 23, 2016; revised August 31, 2016; accepted October 15, 2016. This work was supported by the Portuguese Fundac¸ao˜ para been integrated in state-of-the-art wireless network standards a Cienciaˆ e Tecnologia FCT/MEC through national funds, PURE-5GNET and [7], [8], [9], e.g., IEEE 802.11n, 802.11ac WLAN, 802.16e VELOCE-MTC (UID/EEA/50008/2013) projects. (Mobile WiMAX), 802.16m (WiMAX), 802.20 (MBWA), E. Castaneda,˜ A. Silva, and A. Gameiro are with the Department of Electronics, Telecommunications and Informatics, and the Instituto de 802.22 (WRAN), 3GPP long-term evolution (LTE) and LTE- Telecomunicac¸oes˜ (IT), Aveiro University, Aveiro, 3810-193, Portugal (e-mail: Advanced (E-UTRA). Resource allocation is particularly chal- [email protected]; [email protected]; [email protected]). lenging in wireless communication systems mainly due to M. Kountouris is with the Mathematical and Algorithmic Sciences Lab, France Research Center, Huawei Technologies Co. Ltd. (e-mail: mar- the wireless medium variability and channel randomness, [email protected]). which renders the overall performance location-dependent and 2 tion of different aspects of MU-MIMO systems and resource Applications & Services: QoS requirements allocation schemes. Users with independent channels provide a new sort of diversity to enhance the overall performance. Multiple Access Schemes & Scheduling However, in contrast to systems where each user accesses a dedicated (orthogonal) resource [4], [10], [16], [17], in Optimal Heuristic MU-MIMO systems the additional diversity is realized when several users access the same resource simultaneously. Accounting for multiple antennas at both ends of the radio MU-MIMO Signal: Power & Rate Design & Processing Allocation link allows spatial steering of independent signals using pre- Non-Linear coding schemes, which results in the coexistence of many data Linear Precoding Optimal Ad hoc Precoding streams conveyed to the concurrent users. Some of the main Optimal Sub- Sub- Optimal (DPC) optimal optimal contributions of this survey are the description and classifica- tion of linear and non-linear precoding schemes, considering the amount of channel information available at the transmitter, Fig. 1. Components of the resource allocation policy in MU-MIMO systems the network scenario (e.g. single-cell or multi-cell), and the antenna settings. Each precoding scheme relies on different characteristics of the MU-MIMO channels to fully exploit time-varying [10]. Nevertheless, high spectral efficiency and the spatial domain. The paper provides a comprehensive multiplexing gains can be attained in MIMO systems since classification of metrics that quantify the spatial compatibility, multiple data streams can be conveyed to independent users. which can be used to select users and improve the precoding By exploiting the spatial degrees of freedom (DoF) offered performance. by multiple antennas we can avoid system resource wastage [11]. Multiuser (MU) MIMO systems have been extensively The spectral efficiency, error rates, fairness, and QoS are investigated over the last years from both theoretical and common criteria to assess performance in the MU-MIMO lit- practical perspective. In a recent evolution of MU-MIMO erature. Optimizing each one of these metrics requires specific technology, known as massive MIMO or large-scale MIMO problem and constrains formulations. The type of precoding, [12], [13], few hundreds of antennas are employed at the BS antenna configuration, and upper layer demands can be taken to send simultaneously different data streams to tens of users. into account to design robust resource allocation algorithms, Massive MIMO has been identified as one of the promising i.e. cross layer designs. Other contributions of this paper are air interface technologies to address the massive capacity the description and classification of different optimization cri- requirement required demanded by 5G networks [14], [15]. teria and general constraints used to characterize MU-MIMO The downlink transmission is particularly challenging in system. The proposed classification incorporates the antenna MU-MIMO scenarios because the geographic location of the configuration, the amount of channel information available at receivers is random and joint detection cannot be performed. the transmitter, and upper layer requirements. The main goal is to convey independent data streams to a set of Early surveys on MU-MIMO have pointed out that resource properly selected users, attaining spatial multiplexing gain of- allocation can be opportunistically enhanced by tracking the fered by MU-MIMO. However, determining such a users set is instantaneous channel fluctuations for scenarios with a sin- a very challenging task, which