Junyuan Wang

Asymptotic Rate Analysis of Downlink Multi-User Systems With Co-Located and Distributed Antennas

A great deal of efforts have been made on the performance evaluation of distributed antenna systems (DASs). Most of them assume a regular base-station (BS) antenna layout where the number of BS antennas is usually small. With the growing interest in cellular systems with large antenna arrays at BSs, it becomes increasingly important to study how the BS antenna layout affects the rate performance when a vast number of BS antennas are employed. This paper presents a comparative study of the asymptotic rate performance of downlink multi-user systems with multiple BS antennas either co-located or uniformly distributed within a circular cell. Two representative linear precoding schemes, maximum ratio transmission (MRT), and zero-forcing beamforming (ZFBF), are considered, with which the effect of BS antenna layout on the rate performance is characterized. The analysis shows that as the number of BS antennas L and the number of users K grow infinitely while L/K → v, the asymptotic average user rates with the co-located antenna (CA) layout for both MRT and ZFBF are logarithmic functions of the ratio u. With the distributed antenna (DA) layout, in contrast, the scaling behavior of the average user rate closely depends on the precoding schemes. With ZFBF, for instance, the average user rate grows unboundedly as L, K → ∞ and L/K → v > 1, which indicates that substantial rate gains over the CA layout can be achieved when the number of BS antennas L is large. The gain, nevertheless, becomes marginal when MRT is adopted.

Downlink Rate Analysis for Virtual-Cell Based Large-Scale Distributed Antenna Systems

Despite substantial rate gains achieved by joint transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few neighboring base-station (BS) antennas to form its virtual cell, i.e, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each users virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity.

Low-Complexity Beam Allocation for Switched-Beam Based Multiuser Massive MIMO Systems

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K log N). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N/K.

Beam allocation and performance evaluation in switched-beam based massive MIMO systems

This paper focuses on the beam allocation problem with the target of maximizing the sum rate in a switched-beam based massive multiple input multiple output (MIMO) system working at the millimeter wave (mmWave) frequency band. A simple suboptimal beam allocation algorithm is developed, whose average sum rate performance is shown to be nearly optimal while the complexity is greatly reduced. Different from conventional switched-beam systems, with a large number of beams in the massive MIMO systems, the inter-beam interference closely depends on the beam allocation result. By adopting the suboptimal beam allocation algorithm, the effect of the inter-beam interference is investigated through simulations. The results further show that the average sum rate increases with both the number of BS antenna elements and the number of users even though the inter-beam interference is enlarged with an increasing number of users.

Asymptotic rate analysis for non-orthogonal downlink multi-user systems with co-located and distributed antennas

This paper presents an asymptotic rate analysis for downlink multi-user systems with L base-station (BS) antennas either co-located or uniformly distributed within a circular cell. A representative non-orthogonal linear precoding scheme, maximum ratio transmission (MRT), is considered, based on which the effect of BS antenna layout on the intra-cell interference is characterized. The analysis reveals that the ratio σ of the number of BS antennas L and the number of users K is a key parameter that determines the rate performance of non-orthogonal downlink multi-user systems. Ergodic rates in the colocated antenna (CA) layout and the distributed antenna (DA) layout both logarithmically grow with σ, yet a higher rate is achieved in the DA case thanks to enhanced signal-to-interference ratio.

A Machine Learning Framework forResource Allocation Assisted by CloudComputing

Conventionally, resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to obtain in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of a scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive MIMO systems shows that the proposed machine-learning-based resource

Transmit antenna selection for massive MIMO: A knapsack problem formulation

Massive multiple input multiple output communication is now possible using millimeter wave frequency band. In this paper a transmit antenna selection algorithm is developed which satisfies a quality of service (QoS) for a given user. In order to achieve a particular level of QoS, the number of transmit antennas required is determined by remodeling it as a Knapsack Problem (KP). The smallest subset of antenna elements is found at the transmitter side to achieve the desired level of QoS using KP. Furthermore, we have compared our algorithm with the sorted and unsorted sequential selection algorithm (SSA). Our algorithm achieves similar performance as compared to SSA but with lower computational complexity than the sorted SSA. Moreover, the energy efficiency of our algorithm is similar to that of the sorted SSA but superior to unsorted SSA, as it is not sensitive to the arrangement of the antenna gains.

Frequency Reuse of Beam Allocation for Multiuser Massive MIMO Systems

Massive multiple-input-multiple-output (MIMO) has become a promising technique to provide high-data-rate communication in fifth-generation mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed-beam based multiuser massive MIMO system, where each user is served by a beam allocated to it. To maximize the sum data rate, a greedy beam allocation algorithm is proposed under the practical condition that the number of radio frequency chains is smaller than the number of users. Simulation results show that our proposed greedy algorithm achieves nearly optimal sum data rate. As only the sum data rate is optimized, there are some “worst-case” users, who could suffer from strong inter-beam interference and thus experience low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users’ data rates and the max-min fairness among served users without sacrificing the sum data rate.

Low-Complexity Hybrid Digital-to-Analog Beamforming for Millimeter-Wave Systems with High User Density

Supporting high user density and improving millimeter- wave (mm-Wave) spectral-efficiency (SE) is imperative in 5G systems. Current hybrid digital-to-analog beamforming (D-A BF) base stations (BS) can only support a particular user per radio frequency (RF) chain, which severely restricts mm-Wave SE. In this paper a novel low-complexity selection combining (LC- SC) is proposed for supporting high user density for mm-Wave BS. When compared with the current state of the art hybrid D-A BF, simulations show that LC-SC can support high user density and attain higher SE.

Seamless Switching Using Distributed Antenna Systems for High-Speed Railway

High-speed railway (HSR) has witnessed a huge growth all over the globe reaching a maximum speed of 575 km/h. This record of speed makes mobile communications difficult for HSR since the handover (HO) frequency increases which frequently results in a loss of connectivity. This paper proposes a specialized network architecture based on distributed antenna systems (DAS) for HSR broadband wireless communication systems along with the two- hop architecture. Further, a frequency switch (FSW) scheme is proposed aiming to eliminate the need for HO between the successive small coverage remote antenna units (RAUs) that fall under the same central unit (CU) control. The analytical results show that the proposed scheme outperforms traditional HO schemes and can support the application with high quality of services (QoS) requirement.