text: We propose a concept system termed distributed base station (DBS), which enables distributed transmit beamforming at large carrier wavelengths to achieve significant range extension and or increased downlink data rate, providing a low-cost infrastructure for applications such as rural broadband. We consider a frequency division duplexed (FDD) system, using feedback from the receiver to achieve the required phase coherence. At a given range, @math cooperating transmitters can achieve @math -fold increase in received power compared to that for a single transmitters, and feedback-based algorithms with near-ideal performance have been prototyped. In this paper, however, we identify and address key technical issues in translating such power gains into range extension via a DBS. First, to combat the drop in per-node SNR with extended range, we design a feedback-based adaptation strategy that is suitably robust to noise. Second, to utilize available system bandwidth, we extend narrowband adaptation algorithms to wideband channels through interpolation over OFDM subcarriers. Third, we observe that the feedback channel may become a bottleneck unless sophisticated distributed reception strategies are employed, but show that acceptable performance can still be obtained with standard uplink reception if channel time variations are slow enough. We quantify system performance compactly via outage capacity analyses.
  @cite_30: The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. To reduce the overhead of the downlink training phase, we propose practical open-loop and closed-loop training frameworks in this paper. We assume the base station and the user share a common set of training signals in advance. In open-loop training, the base station transmits training signals in a round-robin manner, and the user successively estimates the current channel using long-term channel statistics such as temporal and spatial correlations and previous channel estimates. In closed-loop training, the user feeds back the best training signal to be sent in the future based on channel prediction and the previously received training signals. With a small amount of feedback from the user to the base station, closed-loop training offers better performance in the data communication phase, especially when the signal-to-noise ratio is low, the number of transmit antennas is large, or prior channel estimates are not accurate at the beginning of the communication setup, all of which would be mostly beneficial for massive MIMO systems.
  @cite_35: Multiple-input multiple-output (MIMO) technology is maturing and is being incorporated into emerging wireless broadband standards like long-term evolution (LTE) [1]. For example, the LTE standard allows for up to eight antenna ports at the base station. Basically, the more antennas the transmitter receiver is equipped with, and the more degrees of freedom that the propagation channel can provide, the better the performance in terms of data rate or link reliability. More precisely, on a quasi static channel where a code word spans across only one time and frequency coherence interval, the reliability of a point-to-point MIMO link scales according to Prob(link outage) SNR-ntnr where nt and nr are the numbers of transmit and receive antennas, respectively, and signal-to-noise ratio is denoted by SNR. On a channel that varies rapidly as a function of time and frequency, and where circumstances permit coding across many channel coherence intervals, the achievable rate scales as min(nt, nr) log(1 + SNR). The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time [2].
  @cite_22: We present joint multi-user beamforming (JMB), a system that enables independent access points (APs) to beamform their signals, and communicate with their clients on the same channel as if they were one large MIMO transmitter. The key enabling technology behind JMB is a new low-overhead technique for synchronizing the phase of multiple transmitters in a distributed manner. The design allows a wireless LAN to scale its throughput by continually adding more APs on the same channel. JMB is implemented and tested with both software radio clients and off-the-shelf 802.11n cards, and evaluated in a dense congested deployment resembling a conference room. Results from a 10-AP software-radio testbed show a linear increase in network throughput with a median gain of 8.1 to 9.4x. Our results also demonstrate that JMB's joint multi-user beamforming can provide throughput gains with unmodified 802.11n cards.
  @cite_15: The enormous success of advanced wireless devices is pushing the demand for higher wireless data rates. Denser spectrum reuse through the deployment of more access points (APs) per square mile has the potential to successfully meet such demand. In principle, distributed multiuser multiple-input-multiple-output (MU-MIMO) provides the best approach to infrastructure density increase since several access points are connected to a central server and operate as a large distributed multiantenna access point. This ensures that all transmitted signal power serves the purpose of data transmission, rather than creating interference. In practice, however, a number of implementation difficulties must be addressed, the most significant of which is aligning the phases of all jointly coordinated APs. In this paper, we propose AirSync, a novel scheme that provides timing and phase synchronization accurate enough to enable distributed MU-MIMO. AirSync detects the slot boundary such that all APs are time-synchronous within a cyclic prefix (CP) of the orthogonal frequency-division multiplexing (OFDM) modulation and predicts the instantaneous carrier phase correction along the transmit slot such that all transmitters maintain their coherence, which is necessary for multiuser beamforming. We have implemented AirSync as a digital circuit in the field programmable gate array (FPGA) of the WARP radio platform. Our experimental testbed, comprising four APs and four clients, shows that AirSync is able to achieve timing synchronization within the OFDM CP and carrier phase coherence within a few degrees. For the purpose of demonstration, we have implemented two MU-MIMO precoding schemes, Zero-Forcing Beamforming (ZFBF) and Tomlinson-Harashima Precoding (THP). In both cases, our system approaches the theoretical optimal multiplexing gains. We also discuss aspects related to the MAC and multiuser scheduling design, in relation to the distributed MU-MIMO architecture. To the best of our knowledge, AirSync offers the first realization of the full distributed MU-MIMO multiplexing gain, namely the ability to increase the number of active wireless clients per time-frequency slot linearly with the number of jointly coordinated APs, without reducing the per client rate.
  @cite_28: Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.
  @cite_21: We report on recent results from our ongoing work on demonstrating retrodirective transmission from distributed arrays. Specifically, we describe a successful experimental demonstration of retrodirective beamforming to a non-cooperating receiver from an array of three distributed transceivers using (mostly) off-the-shelf hardware and simple and standard signal processing techniques. We build on our recently reported work that describes a synchronization procedure that allows the array nodes to estimate and compensate for the combined effects of frequency offsets and drifts in the oscillators as well as nonreciprocal elements in the transceiver hardware. We show how the array nodes that have performed the above synchronization process can then use an opportunistic incoming transmission from an external target to perform retrodirective beamforming back to that target without any coordination with that target. Our experimental results show beamforming gains greater than 90 . A key distinguishing feature of our work is that our procedure requires no wired links between the array nodes, no GPS, nor any other shared signal. To the best of our knowledge, this is the first ever demonstration of retrodirective beamforming from a fully wireless distributed array.
  @cite_1: This paper describes a technique for transmit beam- forming from the nodes in a distributed radio network to a distant target node across a frequency-selective channel. The approach exploits signals transmitted by the target node and channel reciprocity to avoid requiring explicit channel state feedback from the target. Channel estimates between network radios are used for relative calibration to address non-reciprocal effects due to independent clocks and electronic component variability. Variants of the technique allow wideband coherent beamforming to the target when the target signal is known or when it is unknown.
  @cite_32: Recent years have seen a lot of work in moving distributed MIMO from theory to practice. While this prior work demonstrates the feasibility of synchronizing multiple transmitters in time, frequency, and phase, none of them deliver a full-fledged PHY capable of supporting distributed MIMO in real-time. Further, none of them can address dynamic environments or mobile clients. Addressing these challenges, requires new solutions for low-overhead and fast tracking of wireless channels, which are the key parameters of any distributed MIMO system. It also requires a software-hardware architecture that can deliver a distributed MIMO within a full-fledged 802.11 PHY, while still meeting the tight timing constraints of the 802.11 protocol. This architecture also needs to perform coordinated power control across distributed MIMO nodes, as opposed to simply letting each node perform power control as if it were operating alone. This paper describes the design and implementation of MegaMIMO 2.0, a system that achieves these goals and delivers the first real-time fully distributed 802.11 MIMO system.
  @cite_6: We describe the key ideas behind our implementation of a distributed antenna array fully pre-synchronized for retrodirective transmission to an external receiver. In our implementation, a number of wireless transceivers in a network use a sequence of simple in-band wireless message exchanges to calibrate themselves so that these transceivers can obtain their channel gains to an external receiver using reciprocity simply by observing a single incoming transmission from that receiver without any channel feedback or other cooperation from the receiver. Some notable features of our implementation are as follows: (a) it automatically calibrates and corrects for unknown channel gains, oscillator offsets and drifts between the array nodes as well as the effect of non-reciprocal RF hardware; (b) it is fully wireless and endogenous i.e., does not use any wired backhaul connections or side channels including GPS; and (c) it uses simple signal processing on a standard and widely available software-defined radio platform based on off-the-shelf hardware and open-source software. Also, to the best of our knowledge, this is the first ever demonstration of a pre-synchronized distributed array, and thus our implementation serves as a proof-of-concept and allows for the development of more advanced distributed array techniques building on this capability.
  @cite_31: 
summary: Our work, and the related work discussed above, falls into the category of all-wireless distributed beamforming with explicit feedback, which allows flexible deployment of DBS supporting FDD operation. It is worth mentioning recent work on all-wireless distributed beamforming based on channel reciprocity, that relies on tight pre-synchronization of the cooperating nodes to emulate a centralized array with a common time base @cite_1 @cite_6 @cite_21 . Finally, there is also significant recent work on distributed MIMO based on coordination of infrastructure nodes (WiFi access points or cellular base stations) via a fast wired backhaul @cite_31 @cite_15 @cite_22 @cite_32 . Our emphasis is to scale up the number of nodes without fast wired backhaul and achieve massive MIMO gains @cite_28 @cite_30 @cite_35 .