Design and experimental evaluation of a low-complexity spatial combiner for LTE distributed antenna systems
© Soler-Garrido et al.; licensee Springer. 2013
Received: 29 September 2012
Accepted: 18 February 2013
Published: 14 March 2013
This article presents a distributed antenna system (DAS) architecture for small-cell base stations (BTSs), whereby cooperation between the DAS infrastructure and the BTS allows for an increase in performance compared to conventional systems, while at the same time keeping complexity and cost at low levels. Specifically, the article investigates the improvements in uplink physical layer performance achieved by adding an initial antenna combining step in the DAS system before conventional combining and equalization at the BTS. This initial step can be implemented in a very low-complexity fashion by performing all operations in the time domain and using channel state information calculated at the BTS itself. The article presents this technique in the context of an LTE DAS system. Results from both a software simulator and a custom-made hardware prototype are presented, establishing the feasibility of the proposed architecture.
In recent times, multiple antenna systems have been one of the main technological drivers for the evolution of wireless communications. In particular, in cellular systems multiple-input multiple-output (MIMO) techniques have successfully been used to increase cell throughput, link capacity, and/or quality [1–3]. Current network deployments typically resort to the use of distributed radio units (RUs) in order to improve the service provided. The so-called remote radio heads containing antennas and RF front-ends but very little processing power [4, 5] are strategically placed to optimize coverage. These are typically connected to a central unit or base station (BTS) via fibre–optical links, and can significantly increase system capacity and coverage . These distributed antenna systems (DASs) have been proposed as a cheaper alternative to deploying multiple BTSs, for example, in an indoor space to improve coverage [7, 8].
In conventional deployments, BTS and DAS are separate subsystems in the overall network. The DAS can be seen as a fixed infrastructure which routes the signal to and from a third party BTS. Its cost is typically related to the area that has to be covered. The BTS itself can have different capabilities which depend on the requirements, e.g., total capacity, of the particular deployment. In general, larger deployments in terms of area do not necessarily require a more powerful BTS, especially if the number of users, or their throughput requirements, are low.
Specifically, we present a two-stage coherent combining scheme where the two combiners serve different purposes and jointly exploit the gain offered by a potentially very large number of RUs. Furthermore, in order to keep complexity and bill of materials low on the DAS infrastructure side, we explore the possibility to perform the combining stage at the HU only in the spatial domain. Thereafter, the second stage at the BTS performs a conventional frequency selective spatial equalization. Moreover, the combining weight calculation at the HU is assisted by existing channel estimation capabilities at the BTS, reducing complexity further. The HU only performs a simple linear combining operation which does not require the provision of expensive FPGAs or digital signal processors.
Our two-stage DAS concept is applied to a 3GPP LTE Release 8  uplink communication setup as an example. The main deployment scenarios we envisage for our architecture are those typical for DAS systems, i.e., indoor office or residential building, and up to small urban cells, where coverage extension is sought without deploying expensive and heavy-maintenance BTS equipments but rather by using inexpensive DAS infrastructure.
The main contributions of this article are
We provide a description of a DAS for uplink LTE. A (potentially) large number of remote antennas are connected to an HU which processes the signals before passing them on to a BTS. Despite the reduced number of BTS antenna ports, the system is able to exploit the spatial gain provided by the multiple RUs.
As a further simplification, we present the calculation of space-only combiner weights, such that the amount of synchronization, and domain conversions required at the DAS hardware are kept to a minimum.
A hardware test-bed has been developed in order to prove the concept. Measurements using a hardware channel emulator allow for a direct comparison between different system architectures, and it is shown that the proposed DAS modifications offer a significant benefit over conventional systems.
The remainder of the article is organized as follows. A system overview of our DAS architecture and motivation for the choice of the two-stage combiner are given in Section 2. The problem of computing the combiner weights is addressed in Section 3 and simulation results are shown in Section 4. The hardware setup and measurement results are presented in Section 5, and conclusions are drawn in Section 6.
2 DAS uplink combining
As aforementioned, a key advantage of DAS is that geographically distributed RUs can be connected to a single serving BTS in order to exploit the spatial gain. This can particularly be useful when small cells (and including femto- or pico-cells) are deployed in indoor spaces to improve coverage and combat interference. The small size and irregular shape of a small cells make the use of RUs very effective in extending/improving coverage without generating too much interference on adjacent cells. Besides, significant gains can be exploited to boost the received signal without increasing the transmit power.
Although any number of antennas can be used, a practical problem is that existing small-cell BTSs have much more limited capability compared to macrocell BTSs and may not support more than two antenna ports; hence, the challenge is to use a potentially large number N of antennas in a resource-constrained HU where a very limited amount of processing can be done. If cost is to be kept low, implementation of functions such as channel estimation may not be feasible.
Moreover, because of the frequency selectivity of a wireless channel, an optimum combiner should apply different weights for different frequencies; in practice the smallest frequency unit for which a different set of weights can be calculated is a physical resource block (RB). In this case too, a frequency selective combiner for an OFDM-based system requires substantial processing in the frequency domain which entails a range of relatively complex operations such as discrete Fourier transforms (DFT–IDFT pair), synchronisation, frequency offset compensation, etc. In particular, if the objective of the combiner is that of minimising the mean squared error of the useful signal estimate in the presence of unknown interference, additional estimates of the auto- and cross-correlation are required and a matrix inversion operation is necessary to compute the minimum mean-squared error (MMSE) combiner weights for each active RB. In order to avoid placing such high processing demands on the DAS system, we propose the use of space-only weights at the HU, e.g., one set of adaptive weights which applies to all RBs.
Of course, performance of such a simple combiner will be degraded in highly frequency selective channels, especially when using large RB allocations. Simulations and experimental results presented in this article aim to analyse this. It should also be noted that in the multiuser case, the radio resource manager (RRM) at the BTS has to be aware of the DAS combining scheme in order to assign entire subframes to users, or group those with similar propagation conditions, so that they share the available bandwidth at any time instant. In the article, we present single-user results intended to quantify the potential performance improvements at physical layer attainable by the proposed DAS solution, without going into RRM implementation details.
This solution is effectively a two-stage combiner. Assuming two antenna ports at the BTS, an N-input 2-output spatial combiner at the HU, whose aim is to exploit the spatial gain offered by coherent combining of the N RUs is followed by a conventional 2-input 1-output MMSE space-frequency combiner at the BTS, whose main task is to combat noise and unknown interference. This separation of roles between the two combiners is motivated by the choice of a frequency non-selective combiner at the HU. In fact, the limitation of having a single set of weights at the HU for the whole configured bandwidth implies that the HU combiner may have poor noise and interference rejection capabilities if the channel and/or interference are frequency selective across the used spectrum. In this respect, the use of a wideband MMSE solution for the HU combiner is ineffective and may give poor results in most channel conditions as the matrix inversion operation tends to amplify the mismatch between combiner weights and actual auto- and crosscovariances values on each RB. Therefore, our choice for the inner combiner is a simple maximal ratio combiner (MRC).
Of course, in order to calculate weights for the outer HU combiner using channel estimates at the BTS, the latter must have direct (noncombined) visibility of the different N inputs at the HU. To allow this, our solution is to cyclically switch between the N antennas at the HU and feed its signal through to one of the input ports on the BTS. At the same time, the N antennas are combined and the combined signal is passed on to the BTS through the other input port. By doing so, the channel can be estimated periodically for each of the N RU. In the next section, we motivate our choice of average MRC weights applied across the bandwidth.
3 Combiner weights
where γ k is the (instantaneous) SNR on the k th subcarrier and is the noise variance.
from the IDFT. Hence, the weights can be computed as a simple average directly from the frequency response hn,k without the need for IDFTs or eigenvalue decomposition.
4 Simulation results
5 Experimental results
In this section, a hardware prototype of the described uplink LTE DAS system is presented, along with some experiments aimed at quantifying, in practical scenarios and under realistic propagation conditions, the performance gains that can be attained by the HU processing described in previous section, and linking them with the simulation results provided.
5.1 Hardware prototype
The eNodeB is a picoChip PC9609 small-cell LTE development system, with a fully featured Rel. 8 PHY. This system contains two antenna ports and operates in 3GPP band 13, i.e., with uplink centred at 782 MHz and downlink at 751 MHz. The bandwidth employed is 10 MHz. In this case, as we are interested in measuring performance at the physical layer, no protocol stack is employed, using instead a simple PHY driver collecting low level measurements.
At the other end of the system lies an Aeroflex TM500 test UE, which again is operated in HARQ only mode, i.e., it establishes only low level communication with the eNodeB. Most of the settings are manually configured and remain static during the experiments, including uplink and downlink resource allocation, MCS values and transmitter power.
The weight calculation modules updates the weights every millisecond, but CSI for only one of the four branches is updated on every subframe period. In the prototype, this CSI for the antenna port used for training is sampled at the HU from the digital GPIO outputs at the eNodeB baseband chip. The communication is performed in serial mode, with a single coefficient being sent for each RB in 32-bit format (I and Q), followed by a single scaler applied at the BTS across the entire bandwidth.
In the evaluation setup, the BTS records values of postequalizer SNR. It is always using MRC on its two input ports, regardless of whether these come from two antennas or the HU. In both cases, the SNR is calculated by the BTS in a conventional manner by using the MIMO channel estimate, the MRC coefficients and the noise power estimate. The measured results are averaged, and captured at a rate of approximately 3 per second. The results presented compare the distribution of measured SNR for the cases of either a two fixed antenna case or a space-only MRC combiner at the HU. The channel emulator is programmed with the channel taps corresponding to scenario B1 NLOS of the Winner II model . Parameters such as transmit power and uplink MCS are selected in order to achieve a midrange average SNR. Variations on SNR are entirely due to random fading, which in this case is uncorrelated for all the antennas.
In this article, we propose a DAS architecture suitable for small-cell BTSs. It provides a low-complexity solution able to make better use of the spatial gain in the uplink of DAS while still employing low-cost BTS hardware. This is achieved by introducing an initial combining stage at the HU, where signals from different RUs are coherently combined. The article explores the performance of a space-only combiner where the uplink SNR is enhanced by applying a single set of weights across all frequencies at the HU. These results are backed by measurements from a hardware prototype making use of a precommercial Rel. 8 eNodeB, a custom-made RF and digital baseband combiner setup, a test LTE UE and a hardware channel emulator. The experimental results show clear performance gains for the combiner case even when the average received power is the same for all RUs. Moreover, the measurements are in line with simulated results, establishing the feasibility of the proposed architecture in practical real-life deployment in the presence of practical hardware impairments and realistic propagation conditions.
The authors would like to thank the directors at Toshiba’s Telecommunication Research Laboratory in Bristol for their constant support. They would also like to acknowledge the support of the Wireless Systems Laboratory at Toshiba’s Corporate R&D Center, and especially the assistance provided by Dr. Tsuguhide Aoki throughout the entire project. They would also like to acknowledge the work done by Yue Tian during his MSc internship at Toshiba TRL supporting this project.
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