A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends

Multiple antennas have been exploited for spatial multiplexing and diversity transmission in a wide range of communication applications. However, most of the advances in the design of high speed wireless multiple-input multiple output (MIMO) systems are based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. Although the Gaussian signal is capacity-achieving, signals conforming to discrete constellations are transmitted in practical communication systems. As a result, this paper is motivated to provide a comprehensive overview on MIMO transmission design with discrete input signals. We first summarize the existing fundamental results for MIMO systems with discrete input signals. Then, focusing on the basic point-to-point MIMO systems, we examine transmission schemes based on three most important criteria for communication systems: the mutual information driven designs, the mean square error driven designs, and the diversity driven designs. Particularly, a unified framework which designs low complexity transmission schemes applicable to massive MIMO systems in upcoming 5G wireless networks is provided in the first time. Moreover, adaptive transmission designs which switch among these criteria based on the channel conditions to formulate the best transmission strategy are discussed. Then, we provide a survey of the transmission designs with discrete input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, we discuss the transmission designs with discrete input signals for other systems using MIMO technology. Finally, technical challenges which remain unresolved at the time of writing are summarized and the future trends of transmission designs with discrete input signals are addressed.Comment: 110 pages, 512 references, submit to Proceedings of the IEE

Adaptive Mode Selection in Multiuser MISO Cognitive Networks with Limited Cooperation and Feedback

In this paper, we consider a multiuser MISO downlink cognitive network coexisting with a primary network. With the purpose of exploiting the spatial degree of freedom to counteract the inter-network interference and intra-network (inter-user) interference simultaneously, we propose to perform zero-forcing beamforming (ZFBF) at the multi-antenna cognitive base station (BS) based on the instantaneous channel state information (CSI). The challenge of designing ZFBF in cognitive networks lies in how to obtain the interference CSI. To solve it, we introduce a limited inter-network cooperation protocol, namely the quantized CSI conveyance from the primary receiver to the cognitive BS via purchase. Clearly, the more the feedback amount, the better the performance, but the higher the feedback cost. In order to achieve a balance between the performance and feedback cost, we take the maximization of feedback utility function, defined as the difference of average sum rate and feedback cost while satisfying the interference constraint, as the optimization objective, and derive the transmission mode and feedback amount joint optimization scheme. Moreover, we quantitatively investigate the impact of CSI feedback delay and obtain the corresponding optimization scheme. Furthermore, through asymptotic analysis, we present some simple schemes. Finally, numerical results confirm the effectiveness of our theoretical claims.Comment: 11 pages,6 figures, 4 tables IEEE Transactions on Vehicular Technology, 201

Modulation-Specific Multiuser Transmit Precoding and User Selection for BPSK Signalling

Motivated by challenges to existing multiuser transmission methods in a low signal to noise ratio (SNR) regime, and emergence of massive numbers of low data rate ehealth and internet of things (IoT) devices, in this paper we show that it is beneficial to incorporate knowledge of modulation type into multiuser transmit precoder design. Particularly, we propose a transmit precoding (beamforming) specific to BPSK modulation, which has maximum power efficiency and capacity in poor channel conditions. To be more specific, in a multiuser scenario, an objective function is formulated based on the weighted sum of error probabilities of BPSK modulated users. Convex optimization is used to transform and solve this ill-behaved non-convex minimum probability of error (MPE) precoding problem. Numerical results confirm significant performance improvement. We then develop a low-complexity user selection algorithm for MPE precoding. Based on line packing principles in Grassmannian manifolds, the number of supported users is able to exceed the number of transmit antennas, and hence the proposed approach is able to support more simultaneous users compared with existing multiuser transmit precoding methods

Learning-Based Adaptive Transmission for Limited Feedback Multiuser MIMO-OFDM

Performing link adaptation in a multiantenna and multiuser system is challenging because of the coupling between precoding, user selection, spatial mode selection and use of limited feedback about the channel. The problem is exacerbated by the difficulty of selecting the proper modulation and coding scheme when using orthogonal frequency division multiplexing (OFDM). This paper presents a data-driven approach to link adaptation for multiuser multiple input mulitple output (MIMO) OFDM systems. A machine learning classifier is used to select the modulation and coding scheme, taking as input the SNR values in the different subcarriers and spatial streams. A new approximation is developed to estimate the unknown interuser interference due to the use of limited feedback. This approximation allows to obtain SNR information at the transmitter with a minimum communication overhead. A greedy algorithm is used to perform spatial mode and user selection with affordable complexity, without resorting to an exhaustive search. The proposed adaptation is studied in the context of the IEEE 802.11ac standard, and is shown to schedule users and adjust the transmission parameters to the channel conditions as well as to the rate of the feedback channel

Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks

With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: spectrum efficiency (SE) versus energy efficiency (EE), deployment efficiency (DE) versus energy efficiency (EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing (OFDM) and non-orthogonal aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous networks (HetNets). We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks.Comment: revised from IEEE Communications Surveys & Tutorial

Power Efficient Resource Allocation for Full-Duplex Radio Distributed Antenna Networks

In this paper, we study the resource allocation algorithm design for distributed antenna multiuser networks with full-duplex (FD) radio base stations (BSs) which enable simultaneous uplink and downlink communications. The considered resource allocation algorithm design is formulated as an optimization problem taking into account the antenna circuit power consumption of the BSs and the quality of service (QoS) requirements of both uplink and downlink users. We minimize the total network power consumption by jointly optimizing the downlink beamformer, the uplink transmit power, and the antenna selection. To overcome the intractability of the resulting problem, we reformulate it as an optimization problem with decoupled binary selection variables and non-convex constraints. The reformulated problem facilitates the design of an iterative resource allocation algorithm which obtains an optimal solution based on the generalized Bender's decomposition (GBD) and serves as a benchmark scheme. Furthermore, to strike a balance between computational complexity and system performance, a suboptimal algorithm with polynomial time complexity is proposed. Simulation results illustrate that the proposed GBD based iterative algorithm converges to the global optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. Our results also demonstrate the trade-off between power efficiency and the number of active transmit antennas when the circuit power consumption is taken into account. In particular, activating an exceedingly large number of antennas may not be a power efficient solution for reducing the total system power consumption. In addition, our results reveal that FD systems facilitate significant power savings compared to traditional half-duplex systems, despite the non-negligible self-interference.Comment: Submitted for possible journal publicatio

Performance Analysis of Massive MIMO for Cell-Boundary Users

In this paper, we consider massive multiple-input multiple-output (MIMO) systems for both downlink and uplink scenarios, where three radio units (RUs) connected via one digital unit (DU) support multiple user equipments (UEs) at the cell-boundary through the same radio resource, i.e., the same time-frequency slot. For downlink transmitter options, the study considers zero-forcing (ZF) and maximum ratio transmission (MRT), while for uplink receiver options it considers ZF and maximum ratio combining (MRC). For the sum rate of each of these, we derive simple closed-form formulas. In the simple but practically relevant case where uniform power is allocated to all downlink data streams, we observe that, for the downlink, vector normalization is better for ZF while matrix normalization is better for MRT. For a given antenna and user configuration, we also derive analytically the signal-to-noise-ratio (SNR) level below which MRC should be used instead of ZF. Numerical simulations confirm our analytical results.Comment: accepted at IEEE Transaction on Wireless Communicatio

Symbol-level and Multicast Precoding for Multiuser Multiantenna Downlink: A Survey, Classification and Challenges

Precoding has been conventionally considered as an effective means of mitigating the interference and efficiently exploiting the available in the multiantenna downlink channel, where multiple users are simultaneously served with independent information over the same channel resources. The early works in this area were focused on transmitting an individual information stream to each user by constructing weighted linear combinations of symbol blocks (codewords). However, more recent works have moved beyond this traditional view by: i) transmitting distinct data streams to groups of users and ii) applying precoding on a symbol-per-symbol basis. In this context, the current survey presents a unified view and classification of precoding techniques with respect to two main axes: i) the switching rate of the precoding weights, leading to the classes of block- and symbol-level precoding, ii) the number of users that each stream is addressed to, hence unicast-/multicast-/broadcast- precoding. Furthermore, the classified techniques are compared through representative numerical results to demonstrate their relative performance and uncover fundamental insights. Finally, a list of open theoretical problems and practical challenges are presented to inspire further research in this area.Comment: Submitted to IEEE Communications Surveys & Tutorial

Detection and Estimation Algorithms in Massive MIMO Systems

This book chapter reviews signal detection and parameter estimation techniques for multiuser multiple-antenna wireless systems with a very large number of antennas, known as massive multi-input multi-output (MIMO) systems. We consider both centralized antenna systems (CAS) and distributed antenna systems (DAS) architectures in which a large number of antenna elements are employed and focus on the uplink of a mobile cellular system. In particular, we focus on receive processing techniques that include signal detection and parameter estimation problems and discuss the specific needs of massive MIMO systems. Simulation results illustrate the performance of detection and estimation algorithms under several scenarios of interest. Key problems are discussed and future trends in massive MIMO systems are pointed out.Comment: 7 figures, 14 pages. arXiv admin note: substantial text overlap with arXiv:1310.728

Stepwise Transmit Antenna Selection in Downlink Massive Multiuser MIMO

Due to the large power consumption in RF-circuitry of massive MIMO systems, practically relevant performance measures such as energy efficiency or bandwidth efficiency are neither necessarily monotonous functions of the total transmit power nor the number of active antennas. Optimal antenna selection is however computationally infeasible in these systems. In this paper, we propose an iterative algorithm to optimize the transmit power and the subset of selected antennas subject to non-monotonous performance measures in massive multiuser MIMO settings. Numerical results are given for energy efficiency and demonstrate that for several settings the optimal number of selected antennas reported by the proposed algorithm is significantly smaller than the total number of transmit antennas. This fact indicates that antenna selection in several massive MIMO scenarios not only reduces the hardware complexity and RF-costs, but also enhances the energy efficiency of the system.Comment: To be presented in 22nd International ITG Workshop on Smart Antennas (WSA 2018); 8 pages, 3 figure