| Maede Zolanvari (A paper written under the guidance of Prof. Raj Jain) |
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Spectrum efficiency has been the most essential and critical research topic over a few recent decades to improve the operation and development of the mobile communication. With the current increasing number of mobile Internet access, social networks, and smart phones, this concern has become even more crucial. After the discovery of spatial multiplexing via multiple antennas in the early years of 1990s, the hope to be able to improve data rates even within a limited bandwidth came up. The potential of spatial multiplexing can be boosted via MIMO technology through different schemes such as: distributed, cooperative, massive, and full dimension. However, no achievement in the performance can be gained without some cost; this is crucial for any proposed scheme to evaluate the trade-ff that is encountering. In this paper, we briefly talk about each of these new emerging technologies and challenges associated with each.
Keywords: MIMO, Distributed MIMO, Cooperative MIMO, Massive MIMO, Full Dimension MIMO, Spectrum efficiency, Multiplexing, Spatial multiplexing.
The theory and practice of MIMO communication has reached the point where MIMO is now the main infrastructure of several recent WiFi and cellular standards, such as 802.11n, 802.11ac, long-term evolution (LTE), WiMAX, and International Mobile Telecommunications (IMT)-Advanced. Although the advantages of MIMO are substantial, the feasibility of MIMO is almost limited by physical and economic constraints. For instance, the form factor of handheld devices typically limits the number of antennas to only one or two. Even for infrastructure nodes such as access points and base stations, massive MIMO transceivers with a very large number of antennas at current WiFi and cellular frequencies would perhaps be excessively immense and expensive, which increases the processing complexity by some order of magnitude. And also for lower, white space frequencies with carrier wavelengths as large as 6 meters, conventional MIMO is typically impractical. In this paper, I will go through different schemes that lead MIMO techniques into boosted performance and deal with the current challenges.
We can exploit the MIMO benefits such as, boosting transmit/receive directivity, decreasing interference, increasing degrees of freedom and spectral efficiency, and improving spatial diversity by releasing MIMO from traditional form factor limits providing centralized antenna arrays. This is needed to be implemented by employing multiple devices working together to form a virtual antenna array. Moreover, the most effective means of implementing a real massive MIMO system is in a way that the distributed MIMO (DMIMO) scheme can be designed such that it would be insensitive to the number of cooperating nodes, also known as scalable feature [ Madhow14].
Despite any achievement in the performance that any scheme of MIMO systems would provide, there is a trade-off. The boosted performance leads to the increased complexity of the hardware (number of RF chains) and the complexity and energy consumption of the signal processing of the transceivers. Hardware complexity would be shown in the matter of the physical space needed to employ the antennas, including rent of real estate.
For point-to-point links, complexity at the receiver is usually a greater concern than complexity at the transmitter. In multiuser systems, complexity at the transmitter would even cause problems. The main reason is that at the same time, multi-users are trying to send data and some advanced coding schemes must be employed to provide a controlled level of inter-user interference [Rusek12].
However, there are a couple of fundamental problems and challenges in creating the smart networks of coordinated DMIMO nodes from the conventional networks of uncoordinated omni-directional transceivers. We address these challenges as follows:
Synchronizing nodes' oscillators in both frequency and phase is the main challenge that needs to be handled. A good approach for solving this problem is that all the nodes in the cluster try to synchronize to a common beacon. This approach needs to implement many different protocols to provide unique rules and standardize different schema.
The main practical problem is that we never know where the destination is located precisely enough; also, the channel is usually multi-path. These two reasons make the problem of distributed transmit beamforming to a far destination. In theory, we have to know the locations of the transmitter and receiver antennas precisely, plus if there is no multipath, then we can compute the beamforming weights for each transmitter exactly.
Implicit feedback from channel reciprocity can be used along with explicit feedback for time division duplex communication. This approach is particularly attractive in highly mobile settings, where it is hard to keep the explicit feedback updated fast. Retro-directive antenna arrays reflect or retransmit an unwanted wave from a source directly back to the source without any prior knowledge of the source's location. In distributed transmit beamforming scheme, the transmit nodes estimate the phase of a beacon from the intended receiver and respond back with a beam to the destination with no additional overhead.
As we saw at this section, although distributed MIMO can significantly improve the performance, there are a couple of challenges that need to be solved. Challenges are mostly related to design precise oscillators and providing an efficient feedback for the system.
If we add MIMO design to the cooperation communication, we would build a Cooperative MIMO (CMIMO) frame. In this frame, by grouping some wireless devices to operate as virtual multi-antenna nodes, we will build a virtual multi-antenna system. This scheme promises to improve network throughput, conserve energy, and increase network coverage.
In CMIMO, a group of fairly close nodes in a certain area will cooperate in transferring signals with another group of nodes. CMIMO, also known virtual, or networked MIMO, is a method of cooperation, in which several one or two-antenna nodes build a multi-antenna node, called as Virtual Antenna Array (VAA). CMIMO provides more benefits in comparison to the conventional MIMO. For instance, as the paper [ Nguyen13] asserts, by not having a low-rank channel gain matrix, unlike conventional MIMO systems, CMIMO can dynamically select its distributed antennas based on the channel state, so that the spatial multiplexing gain can be better with high-rank channels. Moreover, other advantages of CMIMO includes improving the network lifetime, throughput, and reduces the communication delay. In mobile Ad hoc networks (MANETs), the throughput and transmission delay can also be greatly improved by exploiting CMIMO which has the capability of higher transmission range, higher spectrum efficiency and better interference management. Network lifetime is one of the main performance metric in energy-constrained systems such as Wireless Sensor Networks (WSNs) [ Nguyen14]. Compared with that of a Single-Input Single-Output (SISO) method, by employing CMIMO's higher energy efficiency scheme, the lifetime of a WSN can be boosted by several times.
There are several challenges boosting the performance of CMIMO. We will briefly talk about these issues:
The same as MIMO system, CMIMO systems also use spatial multiplexing techniques, in which multiple VAAs send multiple independent data streams which leads to multiplexing gain (MUX). Suppose that we have T antennas at the transmitter and R antennas at the receiver. The decoding process at an R-antenna receiver VAA can be processed by solving a system of R equations. The number of unknowns is the number of data and interference streams. An interference stream is a stream that has not been sent to that specific receiver VAA. The MUX of that CMIMO link is defined as the maximum number of data streams that can be correctly decoded, and is given by min(T , R). This parameter is also known as the degree of freedom of that particular system. This number can be lower if the channel gain matrix is not full-rank. A famous method to have MUX in both CMIMO and MIMO is to use Vertical Bell Laboratories Layered Space-Time architecture (VBLAST). Furthermore, to guarantee that CMIMO's decoding is feasible, signaling packets need to be exchanged among CNs to ensure the size of the receiving VAA satisfies the degree constraint and would results into a high-rank channel gain matrix to achieve a higher MUX.
The MUX of a MIMO link is bounded above by the number of antennas per node, but the MUX at a CMIMO link is equal to the number of antennas per VAA, which can be changed dynamically by changing the number of Cooperating Nodes (CN) per VAA to prevent more interference streams. However, unlike traditional MIMO, the MUX in CMIMO produces some cooperation overhead. This overhead will be exposed in three different ways:
1. Time or delay overhead.
2. Energy for signaling packets (to coordinate CNs or to obtain Channel State Information (CSI)), which is critical to reach the desired MUX.
3. Cooperation interference from creating and operating VAAs.
Diversity gain (DIV) is defined as the improvement in the received signal- to-noise ratio (SNR). By sending multiple, highly correlated versions of a signal at the transmitter, we can achieve high DIV at CMIMO systems. After the introduction of Space-Time Block Codes (STBCs) for MIMO systems, transmit DIV was accomplished by sending multiple orthogonal versions of the signal in different dimensions, before that, systems used multiple antennas at transceivers to have DIV. To exploit DIV in CMIMO, cooperation is performed by employing Differential Space-Time Block Coding (DSTBC), which leads a maximum possible diversity gain of T*R. Like MUX, DIV in CMIMO involves cooperation overhead to jointly encode/decode signals at various CNs. One key application of CMIMO's DIV is to conserve energy. Hence, CMIMO is of great interest to energy-constrained networks, e.g., WSNs.
Viewed as a means to improve signal quality, CMIMO's DIV can also be exploited to extend the transmission range. At a given power budget, the transmission range can be extended by a factor of (√R+ √T )2)(1/α), where α is the free-space attenuation factor. Range extension can be used to enhance the network connectivity in MANETs (topology control) or to improve network coverage in WLANs and cellular networks.
Paper [ Nguyen13] asserts:Information theorists pointed out that in dense networks, due to severe interference, the capacity of each link decreases in proportion to the square root of node density.However in this situation, CMIMO provides several benefits through the cooperation scheme to overcome the interference provided by interference alignment and interference cancellation techniques. In interference alignment, a transmitting VAA aligns several transmitted streams so that a receiving VAA receives only one interference stream. In this case, an R-antenna VAA can simultaneously receive (R - 1) data streams, regardless of the number of interference streams. Interference cancellation is the technique employed at the receiver where several CNs exchange their decoded streams so that these streams would be removed.
Cooperation MIMO was the topic of this section. The main idea behind this scheme is to increase gain of the system by combining the cooperation scheme and MIMO scheme. However, there are challenges that need to be fixed and many researches are working on this topic to provide a system that would manage the interference and can send data to a longer distance.
Massive MIMO (which can also be referred as Large-Scale Antenna Systems, Very Large MIMO, Hyper MIMO and Full-Dimension MIMO) potentially provides orders of magnitude of improvement in throughput and energy efficiency through the use of a very large number of antennas (e.g., hundreds or thousands) that are operated fully coherently and adaptively [Larsson14]. Massive MIMO was firstly employed for Time Division Duplex (TDD) operation, but can potentially be applied also in Frequency Division Duplex (FDD) operation.
The main motivation behind massive MIMO system is that when you reach a critical number of antennas (around 60), the system's energy and spectral efficiency increase significantly. Considering energy and spectral efficiency as the two top priorities in wireless networks, the research community is now looking to design and improve physically implementable schema of this technology [Nutaq].
Massive MIMO also provides other benefits such as: the extensive use of inexpensive low-power components, reduced latency, simplification of the Media Access Control (MAC) layer, and robustness of interference and jamming. An interesting point about Massive MIMO is that experiments have so far not yet disclosed any limitations on the anticipated throughput which is fairly dependent on the propagation environment providing asymptotically orthogonal channels to the terminals.
By employing Zero Forcing (ZF) or Maximum Ratio Transmission (MRT) in the scattered environment, performance of massive MIMO can be boosted. In practice, if more than one antenna exist in the orthogonal channels, the reception and transmission data lack the channel coherence time. Systems exploit this property to have the orthogonality optimal multiplexing. Hence, as paper [Choudary14] declares: It can be argued that in the current text of disruption of emerging technologies massive-MIMO is the best choice for future generation wireless evolution for 5G .
One of the main challenges for these systems is designing a very efficient, low-latency, high-throughput data interface between the central processing unit and the multiple transceivers. As we know, we must increase the frequency to very high value in order to be able to reduce the size of the antennas. This is a main issue since massive MIMO needs to be implemented of a very large scale. Moreover, to increase the data throughput over the RF link, a wide bandwidth is also necessary. RF front-end would specify the frequency coverage. Also, a Wideband system results in a high data throughput, which will lead another challenge for this system [Nutaq].
Moreover, as the number of antennas gets too large at Massive MIMO, the complexity of signal processing such as: Transmit Precoding (TPC), channel estimation and detection is really high. The performance of these systems is constrained by pilot contamination due to pilot reuse in multi-cell scenarios. Moreover, compared to the Physical Downlink Shared Channel (PDSCH) employing either precoding or beamforming, the Signal-to-Interference-plus-Noise Ratio (SINR) of the Physical Broadcasting Channel (PBCH) is lower due to the omni-directional signal transmission [Zheng15].
In Massive MIMO, to take advantage of antenna arrays, all data should be analyzed by a processing unit. However, there are some types of systems with distributed processing units that make them route the data to all these units [Nutaq].
Any system based on the Massive MIMO is usually considered to require complex signal processing. Hence, much research has focused on streamlining and optimizing both signal processing algorithms and their implementations. However, low-complexity algorithms generally decrease the performance. This trade-off is the most commonplace scenario of any complex wireless system. For instance, the more accurate CSI (which boosts the performance), the higher processing complexity. Meanwhile, low-complexity linear channel estimation can be compensated by pilot contamination. However, pilot contamination can be used to overcome using complex channel estimation algorithms. There also exists a trade-off between the complexity of channel prediction as well as the TPC design and the experienced channel, in dynamic scenarios in a cell. Therefore, it is crucial to come up with simple and efficient algorithms to be implemented for channel estimation, channel prediction, TPC and detection [Zheng15]. As an another example, simple linear signal processing approaches, such as Matched-Filter (MF) precoding/detection, can be used in massive MIMO systems to achieve some level of acceptable performance [Lu14].
To exploit the benefits of having precoding in the downlink and detection in the uplink, we need to have some sense of the channel at the Base Station (BS). The resource, time or frequency, required for channel estimation in a MIMO system is proportional to the number of the transmit antennas and is independent of the number of the receive antennas.
In the FDD scenario, in which the uplink and downlink use different frequency bands, the CSI for each uplink and downlink is different. The BS is responsible for the uplink channel estimation by letting all users send different pilot sequences. The time required for uplink pilot transmission is independent of the number of antennas at the BS. Meanwhile, in FDD systems, a two-stage procedure will be done to estimate the channel. The BS first transmits pilot symbols to all users, and then all users send back an estimated CSI for the downlink channels to the BS. The time required to transmit the downlink pilot symbols is related to the number of antennas at the BS.
However, the scenario in TDD is much simpler. We assume that because of channel reciprocity, only CSI for the uplink needs to be estimated. In this case, all the users in all the cells send uplink data signals followed by pilot sequences at the same time. BSs use these pilot sequences to estimate the CSI to the users located in their cells. Then, BSs use the estimated CSI to detect the uplink data and to generate beamforming vectors for downlink data transmission. However, due to the limited channel coherence time, the pilot sequences employed by users in neighboring cells may no longer be orthogonal to those within the cell, leading to a pilot contamination problem. As mentioned before, to provide a low complex algorithm, linear Minimum Mean Square Error (MMSE) based channel estimation is mainly used, providing near-optimal performance with low complexity.
Since the number of orthogonal pilots is smaller than the number of users in a cell, in a typical multi-cell massive MIMO system users from neighboring cells may use non-orthogonal pilots. This issue of using non-orthogonal pilots results in pilot contamination. Pilot contamina- tion causes inter-cell interference. This type of interference is even worse than other sources of interference, because it grows faster with the number of BS antennas and damages the system performance significantly. To resolve this problem, different channel estimation, precoding, and cooperation methods have been proposed. However, more efficient methods with good performance, low complexity, and limited or zero cooperation between BSs need to be studied more in depth. You can see an illustration of pilot contamination at the Figure 1, as you see in the picture, the dash arrows show interference that may happen in neighboring cells.
In this section, we talked about Massive MIMO and challenges on the way to boost the performance of these systems. Pilot contamination is one of the main aspects that should be considered in these type of systems. It would help to decrease the complexity of systems, but it can also lead to interference
In small cell concept, the elevation scale can not be neglected, because this dimension would be comparable with the horizontal scale. This issue leads the Two-Dimensional (2D) MIMO techniques be sub-optimum versus Three-Dimensional (3D) schemes to model the real-world channel. Furthermore, in cases where the mobile users are distributed in 3D space (specially at dense urban areas, including both residential and business districts), managing the system throughput and interference would be more efficient by 3D modeling [Cheng14]
As shown in Figure 2, for covering an area, a wide 2D beam with a fixed elevation angle is divided to multiple 3D beams with dynamic elevation angles.
Based on the potential boosted performance of the full dimension MIMO, Long Term Evolution (LTE), LTE-advanced, and beyond, all use this technology as the main stream. For example, the LTE standard allows for up to 8 antenna ports at the base station. Moreover, it has a groundbreaking potential for the future 5G wireless communication [Xingwang14]. The most recent technology that makes 3D MIMO practical is employing Active Antenna System (AAS). In September 2011 the employment of AAS at BSs was approved by the 3rd Generation Partnership Project (3GPP) at Technology Sciences Group (TSG) Radio Access Network (RAN) #53. This technology integrates power amplifiers and transceivers with the antenna elements. This technology provides more flexible and intelligent beam forming, mainly because it enables the phase and the amplitude of the signals for each antenna element to be controlled electronically, thus resulting in increased capacity and coverage. The antenna radiation pattern can be dynamically controlled in both horizontal and vertical dimensions; hence, it is a perfect method to enable 3D MIMO [Cheng14].
The investigation of full dimension MIMO has strong research interest in both academia and industry. However, much more effort is needed for practical applications of this technology in the near future [Zheng15]. Some of the challenges for these systems are as follows:
One challenge associated with these systems is to find a practical antenna configurations, because they affect the channel properties, their array gains, diversity gains and multiplexing gains. Moreover, AAs may be constrained to a limited physical size. Therefore, the employment of antennas has to be carefully designed to boost the performance as much as possible given the physical area constraint [Zheng15].
As a matter of fact, we need to compute the elevation component as well as other dimensions for these systems, hence challenge of channel estimation with 3D MIMO is still at a very early stage unlike the 2D MIMO. For modeling the 3D channel, only a few qualified channel sounders can be employed. As [Cheng14] asserts: the PropSound channel sounder by Elektrobit and the RUSK MIMO channel sounder by Medav, and also recently one of the leading mobile device manufacturers, Huawei, plans to design and produce their own channel sounder for 3D MIMO measurements. There are several measurement campaigns conducted to measure the 3D MIMO propagation channels in different cases. These mostly focus on the elevation related channel parameters, for example, Elevation Angles of Departure (EAOD) and Elevation Angles of Arrival (EAOA), meanwhile their impact on other important parameters, for example, polarization, Doppler, and power delay profile, have not yet been investigated [Cheng14].
Two important categories of Full Dimensional MIMO systems are either centralized or distributed antenna deployments. However, centralized systems are more popular for being exploited. The main reasons are because DASs improve both the coverage quality and the capacity of wireless communications networks. The coverage quality of both indoor and outdoor environments has to be evaluated in terms of the Probability Density Function (PDF) of their SINRs. For analyzing the performance of wireless networks, stochastic geometry has been widely used, with an emphasis on the 2D plane. Hence, stochastic geometry needs to be extended to the 3D space to analyze large-scale DASs [Zheng15].
In theory, these systems should be built of large numbers of elements to average out noise, channel fading characteristics, etc. However, in practice, low-cost imperfect components are employed to implement the system. Therefore, the imperfections of non-ideal hardware, such as the non-linearities of the amplifier, I/Q imbalance, and A/D and D/A nonlinearities should be considered. Some existing studies have reported on the effects of non-ideal hardware, but still more efforts are required for designing efficient algorithms to mitigate these non-ideal factors [Zheng15].
In this section we talked about full dimension MIMO and related challenges. As you see, challenges are mostly associated with the hardware aspect of these systems. The main effort is to design efficient AAs and DASs, and also to find a way to cope with the fact that hardware is not ideal as we have in theory.
With the evolution of smart terminals and different applications, the need for multimedia services rapidly increases. Also, the load of wireless communications networks is increasing exponentially. Thus, in order to guarantee the Quality of Service (QoS) requirements of any specific application, the capacity of wireless communications networks has to be increased.
To meet the demands of wireless data, 3GPP, LTE and LTE-Advanced Releases 8 through 11 have suggested using the MIMO scheme, coordinated multipoint (CoMP) transmission/reception, and heterogeneous networks tech- niques to improve spectral efficiency [Nam13]. However, to follow the growing future demands for cellular wireless communications, new technologies to fur- ther boost the spectral efficiency are needed. Hence, in this paper, we discussed some new technologies whose main ideas originated with conventional MIMO, but in a way to compromise the flaws in the old scheme.
| [Wiki] | Wikipedia https://en.wikipedia.org/wiki/Cooperative_MIMO |
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| [Rusek12] | F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, " Scaling up MIMO: Opportunities and challenges with very large arrays ", IEEE Signal Process. Mag., 2012, http://arxiv.org/pdf/1201.3210.pdf |
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| 2D | Two-Dimensional |
| 3D | Three-Dimensional |
| 3GPP | 3rd Generation Partnership Project |
| AA | Antenna Array |
| AAS | Active Antenna System |
| BS | Base Station |
| CMIMO | Cooperative MIMO |
| CN | Cooperating Node |
| CoMP | Coordinated Multipoint |
| CSI | Channel State Information |
| DAS | Distributed Antenna Systems |
| DIV | Diversity gain |
| DMIMO | Distributed MIMO |
| DSTBC | Differential STBC |
| EAOA | Elevation Angles of Arrival |
| EAOD | Elevation Angles of Departure |
| FDD | Frequency Division Duplex |
| IMT | International Mobile Telecommunications |
| LTE | Long Term Evolution |
| MAC | Media Access Control |
| MANETM | Mobile Ad hoc Networks |
| MF | Matched-Filter |
| MMSE | Minimum Mean Square Error |
| MRT | Maximum Ratio Transmission |
| MUX | Multiplexing gain |
| PBCH | Physical Broadcasting Channel |
| Probability Density Function | |
| PDSCH | Physical Downlink Shared Channel |
| QoS | Quality of Service |
| RAN | Radio Access Network |
| SINR | Signal-to-Interference-plus-Noise Ratio |
| SISO | Single-Input Single-Output |
| SNR | Signal- to-Noise Ratio |
| STBC | Space-Time Block Code |
| TDD | Time Division Duplex |
| TPC | Transmit Precoding |
| TSG | Technology Sciences Group |
| VAA | Virtual Antenna Array |
| VBLAST | Vertical Bell Laboratories Layered Space-Time |
| WSN | Wireless Sensor Networks |
| ZF | Zero Forcing |