A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
Abstract
:1. Introduction
Notations
2. Basic Concepts
2.1. Uplink Transmission
2.2. Downlink Transmission
3. Advantages and Challenges
3.1. Advantages
- High spectral efficiency: In massive MIMO, using large number of BS antennas and having aggressive spatial multiplexing gives the ability to the users to simultaneously use the same frequency band without interference. This increases the spectral efficiency of the system.
- High power efficiency: Since in massive MIMO we have large number of BS antennas, the energy can be concentrated in a small area and this increases the power efficiency. In these systems the users can reduce their transmit power as we increase the number of BS antennas, while being able to have a performance similar to the SISO case.
- High degree of freedom: Massive MIMO systems have high degree of freedom. For example if we have 200 antennas at the BS and 20 single antenna users, then we have 180 degree of freedom which can be used to help reduce the interference. In other words, we can design the transmitted signals from the BS antennas in a way that all these signals are added constructively at the users and destructively almost anywhere else.
- Enabling reduction of latency: Fading limits the performance of wireless communications systems and makes building low latency links hard. When a user experiences a fading dip it has to wait for suitable change of the channel before receiving the data. However, massive MIMO systems avoid fading dip by having large number of BS antennas and, therefore, enable reduction of latency.
3.2. Challenges
- Antenna array design: Configuration and orientation of antenna arrays in massive MIMO systems is a problem which needs further studies. For example it should be investigated whether 2 dimensional or 3 dimensional antennas are suitable. Also having centralized or distributed antenna arrays and the distance between the antennas and the mutual coupling effect should be studied.
- Implementation in FDD mode: The performance of massive MIMO systems is very dependent on the channel knowledge at the BS. In the uplink the users send orthogonal pilots and the BS estimates the channel of each user considering the pilots. In the downlink of conventional MIMO systems, the BS sends pilots to the users and the users feedback the channel information to the BS, after estimating it. However, since the number of resources needed for channel estimation is proportional to the number of antennas at the transmitter, estimating the channel in downlink of massive MIMO systems where we have large number of antennas at the BS is not suitable and it requires many resources. Also in these systems, the number of channels which each user has to estimate is large and, therefore, a large amount of information has to be fed back to the BS. One solution is to use time division duplex (TDD) mode and take advantage of channel reciprocity to estimate the downlink channel in the uplink. However, this mode also has some drawbacks, for example the hardware impairments in the BS and user side may affect channel reciprocity, not to mention possible channel variations between the time it is estimated and the time it is used (i.e., channel aging effects). Also many current systems work with FDD mode, and for massive MIMO to be compatible with these systems we need to be able to implement it in FDD mode. So, implementing massive MIMO in FDD mode with low training and feedback overhead is an issue that needs further studies.
- Pilot contamination: In TDD mode, each user sends an orthogonal pilot for channel estimation. Since the coherence time of channel is limited, there is a limited number of orthogonal pilots and therefore users in other cells have to reuse these pilots. So, the estimated channel of a specific user will be contaminated with a linear combination of the channel of other users which use the same pilot. Since the beamforming is done considering these estimated channels, an interference between users who use the same pilot will occur.
- Channel model: Most of the works on massive MIMO systems are done with the assumption of time invariant flat rayleigh fading channels. Since in OFDM systems the channel over each sub-carrier is considered flat, the studies on flat fading channels can be extended to OFDM systems. However, in many practical scenarios, the channel is frequency selective and time variant. The channel coefficients can also have different distributions. So, studying these systems in more realistic environments is an issue which needs further investigations.
- Waveform: In 5G, to reach the required data rate and latency expected for future wireless communications the structure of the wireless communication needs significant changes. In other words, 5G is not a simple extension of 4G and it should use some new technologies and integrate different wireless access technologies [23]. Massive MIMO is one of these technologies. In each of these technologies the suitable waveform in different scenarios has to be investigated. Although in 2016 3GPP decided to study various features of 5G new radio (NR) assuming OFDM, but this can change if significant gains can be demonstrated by any other waveform [24,25,26]. Some of the reasons that OFDM is selected for NR uplink and downlink are [27]
- -
- OFDM is a scalable waveform with low implementation complexity,
- -
- Compatibility with multi-antenna technologies,
- -
- High spectral efficiency.
However, OFDM has some drawbacks such as lower frequency localization and high PAPR. Therefore, although an early decision was taken to use the OFDM as 5G waveform, the exact waveform has not yet been decided and other waveforms such as SC-FDMA (also denoted DFT spread OFDM) are also being investigated [24]. - Hardware impairments: Since in massive MIMO systems we have large numbers of antennas, we need to use low-cost hardware to have a cost effective system. This means that hardware impairments will appear, which affect the performance of the system. Therefore, analyzing the performance of massive MIMO systems in presence of hardware impairments and proposing low complexity techniques for compensating them is crucial.
4. Waveform Selection
4.1. Candidate Waveforms
4.1.1. OFDM
4.1.2. OFDMA
4.1.3. SC-FDP
4.1.4. SC-FDMA
4.2. Studies on Multi-Carrier Waveforms
4.3. Studies on Single Carrier Waveforms
5. Hardware Impairments
5.1. Carrier Frequency Offset (CFO)
5.2. Power Amplifier Distortion (PAD)
5.3. I/Q Imbalance
5.4. Phase Noise
6. Time Varying Channels
6.1. Time Varying Flat Fading Channels
6.2. Time Varying Frequency Selective Channels
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Challenge | Paper |
---|---|
Waveform | [16] |
Detection and Precoding | [18,19] |
Hardware impairment | This survey |
Channel measurement and modeling | [17] |
Channel aging and time variation | This survey |
Pilot contamination | [20] |
Impairment | Effect | Possible Solution |
---|---|---|
CFO | - Performance degradation | - Using low complexity CFO estimation techniques |
- Sum-rate limitation in the OFDM case | - Using less sensitive waveforms such as SC-FDP | |
PAD | - Performance degradation | - Using Precoders that produce constant envelop signals |
- Sum-rate limitation | - Using less sensitive waveforms such as SC-FDP | |
- Out of band radiation | - Using clipping and system degree of freedom to compensate clipping distortion | |
I/Q Imbalance | - Performance degradation | - Using Precoders that analyze real and imaginary parts separately |
- Sum-rate limitation | ||
- Inaccurate channel estimation | ||
Phase noise | - Performance degradation | - Using more robust precoders |
- Channel aging | - Using phase noise tracking techniques | |
Channel Aging | - Performance degradation | - Using channel prediction techniques |
- Sum-rate limitation in the OFDM case | - Using less sensitive waveforms such as SC-FDP |
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Mokhtari, Z.; Sabbaghian, M.; Dinis, R. A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments. Sensors 2019, 19, 164. https://doi.org/10.3390/s19010164
Mokhtari Z, Sabbaghian M, Dinis R. A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments. Sensors. 2019; 19(1):164. https://doi.org/10.3390/s19010164
Chicago/Turabian StyleMokhtari, Zahra, Maryam Sabbaghian, and Rui Dinis. 2019. "A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments" Sensors 19, no. 1: 164. https://doi.org/10.3390/s19010164
APA StyleMokhtari, Z., Sabbaghian, M., & Dinis, R. (2019). A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments. Sensors, 19(1), 164. https://doi.org/10.3390/s19010164