Chapter 6 Digital Beamforming 135
6.4 Digital Beamforming in MIMO Transmission
In previous section, we have assumed that there is only one signal of interest. It has been shown that the signals from each antenna element are multiplied by a complex weight and summed to form the array output. In fact, there are more than one signals of interest in MIMO WLAN. In addition, the channel status in MIMO WLAM is complex. Under this condition, it is difficult to find a unique weight vector to form the desired output. transmitter transmits a different data stream. At the receiver part, the received signal is expressed as
1 11 1 12 2
where “ ⊗” denotes the convolution operator. From (6.26), it is difficult to find an
optimum weighting function to attain the desired output s t1( ) and s t 2( ) simultaneously. In practical applications, the channel status can also change with time.
Figure 6-13 shows the channel with angle-time pattern. Form Figure 6-13, the channel pattern could be time-varying and the arrival time of desired signals may also be different. In order to overcome this situation, a potential solution is to augment the simple linear combiner with space-time architecture shown in Figure 6-14. In Figure 6-14, each receiver antenna’s output is applied to a finite impulse filter. The filter outputs are summed to produce the desired signal. If there is no temporal filter, the space-time beamforming architecture is reduced to the linear combiner architecture.
The filter bank-based beamforming can accept the outputs of N antenna elements and coherently combine them to a desired output. In addition, the weighting functions could null out the undesired signals impinging on the array and it could equalize the effects of propagation on the received signals. In practice, however, such joint reduction is limited by the presence of noise and the lack of perfect synchronization.
Figure 6-13. Channel with angle-time pattern.
1( )
r t r ti( ) r tN( )
( ) y t
Figure 6-14. Space-time beamforming architecture.
6.5 Summary
This chapter presents the digital beamforming technology in wireless communication systems. Digital beamforming technology has numerous benefits in wireless communications. In wireless communications, digital beamforming can improve the signal-to-noise ratio and provide higher system capacity. Digital beamforming can be interpreted as linear filtering in the spatial domain. In mobile communications, digital beamforming technology has the potential for tracking the location of a particular mobile user. In addition, digital beamforming technology can also null multipath signals.
This can dramatically reduce fading in the received signal.
Chapter 7 Conclusion
The research on multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems is presented in this dissertation. We investigate the carrier frequency offset (CFO) estimation, I/Q mismatch (IQ-M) estimation, adaptive channel estimation, and digital beamforming.
7.1 Summary
In this work, a MIMO-OFDM communication system is implemented. For the frequency synchronization in the MIMO-OFDM system, a pseudo CFO (P-CFO) algorithm is developed to estimate the CFO value under the condition of IQ-M in direct-conversion OFDM receivers. The proposed synchronization algorithm is suitable for application-specific integrated circuit (ASIC) implementation. The proposed algorithm adopts three short training symbols to estimate the frequency offset from
4 4×
− ppm to 50 ppm under a 2.4 GHz carrier frequency with 2 dB gain error and 20 degree phase error in frequency-selective channels. Simulation results indicate that the average estimation error of the proposed P-CFO algorithm can fulfill many system requirements, preventing obvious performance loss under different IQ-M conditions.
The proposed design is implemented in a chip with +50
3.3 0.4 mm× 2 core area and 10 mW power dissipation at 54 Mbits/s data rate. Hence, the proposed algorithm can enhance the performance of wireless OFDM systems, enabling low-cost systems to be achieved.
Direct-conversion architecture is one potential candidate for simple integration among different architectures. However, direct-conversion receivers suffer from mismatch between the I and Q channels, e.g., IQ-M. In order to combat IQ-M with CFO in direct-conversion receivers, preamble-assisted methods are developed. IQ-M with CFO can be estimated by taking advantage of the relationship between desired sub-carriers and image sub-carriers. Moreover, the proposed IQ-M estimator can estimate not only constant IQ-M but also frequency-dependent IQ-M. Both simulation and experiment results indicate that the proposed method can meet system requirements to prevent from an obvious performance loss under the condition of IQ-M.
Furthermore, the proposed method is compatible with current wireless standards because it does not require special packet formats.
In order to realize the gain obtained from MIMO cannels, an adaptive frequency-domain channel estimator (FD-CE) is developed. The proposed adaptive FD-CE ensures the channel estimation accuracy in each set of four MIMO-OFDM symbols. Without both specific formats and scattered pilots, all data carriers can be utilized to ensure accurate estimation of channel variations, namely, virtual pilots.
Moreover, the proposed channel estimator utilizes the property of the Alamouti-like matrix to decrease the implementation costs of complex operators. Performance
evaluations indicate that the proposed FD-CE can be widely applied in time-varying environments. Consequently, the MIMO-OFDM modem implemented using an in-house 0.13-μm 1P8M CMOS library occupies an area of and consumes about 62.8 mW at 1.2V supply voltage.
4 4×
4.6 4.6× mm2
Digital beamforming technology uses sophisticated signal processing techniques to manipulate signals at the transmitter or receiver and dynamically control transmission and reception. Using digital beamforming technology, radio transmission and reception is optimized by selectively amplifying signals to and from users of interest and rejecting unwanted signals. This mainly increases the signal quality and suppresses and mitigates interference, resulting in increased coverage and system capacity.
7.2 Future Work
At the end of this dissertation, a number of open issues are discussed. Next-generation wireless systems are required to support higher rates, better reliability, and higher mobility while targeting lower cost, lower power consumption, and higher levels of integration [89]. In order to support higher data rate, the trend is to operate at higher carrier frequencies and use higher-order signal constellations which are more sensitive to analog front-end impairments. In this study, we have studied joint effects of IQ-M and CFO. However, there are other issues, such as dynamic acquisition errors of analog-to-digital convertors and amplifier nonlinearity, which could be handled in the digital domain. It is worthy of developing smart signal processing for analog front-end impairments.
Due to the development of new wireless technologies and the improvement of existing ones, the number of users, the demand for spectrum efficiency, and the demand
for higher data rate are increasing. However, the spread of the technology brings different interference sources or jamming [90], [91]. Interference or jamming can interfere with others’ radio and degrade the communication quality. In order to minimize the jamming level and recover the transmitted signal, an appropriate anti-jamming scheme must be applied.
In practice, a communication system can be modeled by a layered approach, such as physical layer and medium access control (MAC), where each layer has a specific role.
The role of the physical layer is to deliver information bits across a wireless channel in an efficient and reliable manner given a limited bandwidth or transmit power. On the one hand, the MAC layer is response for the resource management among multiple users. In traditional designs, there is no cross-optimization across layers. However, optimizing the individual layer is not always the best approach from a system performance perspective. In order to achieve cross-layer optimization, an adaptive cross-layer approach is thus required [92].
With the rapid evolution of wireless standards and increasing demand for multi-standard products, the need for flexible baseband solutions is growing. Efficiently programmable baseband processors are important for multi-standard radio platforms and software defined radio systems. In order to save development cost and silicon area for multi-standard systems, novel baseband processing and efficient VLSI architecture are worthy of developing.
In addition, mobile communications have evolved rapidly in recent years. The existing 3G standard, universal mobile telecommunication system (UMTS), is currently being upgraded with high speed packet access. The 3rd Generation Partnership Project (3GPP) has investigated the long term evolution (LTE) of UMTS to meet future demands. In LTE, it will introduce new access schemes on the air interface, e.g., orthogonal frequency-division multiple access (OFDMA) in downlink and single
carrier – frequency division multiple access (SC-FDMA) in uplink. LTE will also use MIMO to support peak data rates of 100 Mbps in downlink and 50 Mbps in uplink within a 20 MHz spectrum with two receive antennas and one transmit antenna at the user equipment. Hence, LTE is going to be a promising research issue.
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