I/Q imbalance mitigation for space-time block coded communication systems

Multiple-input multiple-output (MIMO) space-time block coded (STBC) wireless communication systems provide reliable data transmissions by exploiting the spatial diversity in fading channels. However, due to component imperfections, the in-phase/quadrature (I/Q) imbalance caused by the non-ideal matching between the relative amplitudes and phases of the I and Q branches always exists in the practical implementation of MIMO STBC communication systems. Such distortion results in a complex conjugate term of the intended signal in the time domain, hence a mirror-image term in the frequency domain, in the data structure. Consequently, I/Q imbalance increases the symbol error rate (SER) drastically in MIMO STBC or STBC MIMO orthogonal frequency division multiplexing (OFDM) communication systems, where both the signal and its complex conjugate are utilized for the information transmission, hence should be mitigated effectively. In this dissertation, the impact of I/Q imbalance in MIMO STBC systems over flat fading channels, the impact of I/Q imbalance in STBC MIMO-OFDM systems and in time- reversal STBC (TR-STBC) systems over frequency-selective fading channels are studied systematically. With regard to the MIMO STBC and the STBC MIMO-OFDM systems with I/Q imbalance, orthogonal space-time block codes (OSTBCs), quasi-orthogonal STBCs (QOSTBCs) and rotated QOSTBCs (RQOSTBCs) are studied, respectively. By exploiting the special structure of the received signal, low-complexity solutions are provided to mitigate the distortion induced by I/Q imbalance successfully. In addition, to mitigate I/Q imbalance while at the same time to exploit the multipath diversity for STBC OFDM systems over frequency-selective fading channels, a new encoding/decoing scheme for the grouped linear constellation precoded (GLCP) OFDM systems with I/Q imbalance is studied. In Chapter 1, the objectives of the research are elaborated. In Chapter 2, the various I/Q imbalance models are introduced, and the model used in this dissertation is established. In Chapter 3, the performance degradation caused by I/Q imbalance of the transceivers in MIMO STBC wireless communication systems over flat fading channels and the solutions are studied. A 2 Tx Alamouti system, a 4 Tx quasi-orthogonal STBC (QOSTBC) system, and a 4 Tx rotated QOSTBC (RQOSTBC) system with I/Q imbalance are studied in detail. By exploiting the special structure of the received signal, low-complexity solutions are proposed to mitigate I/Q imbalance successfully. Since STBCs are developed for frequency-flat fading channels, to achieve the spatial diversity in frequency-selective fading channels, MIMO-OFDM arrangements have been suggested, where STBCs are used across different antennas in conjunction with OFDM. In Chapter 4, the performance degradation caused by I/Q imbalance in STBC MIMO-OFDM wireless systems over frequency-selective fading channels and the solutions are studied. Similarly, a 2 Tx Alamouti system, a 4 Tx quasi-orthogonal STBC (QOSTBC) system, and a 4 Tx rotated QOSTBC (RQOSTBC) system with I/Q imbalance are studied in detail, and low-complexity solutions are proposed to mitigate the distortion effectively. However, OFDM systems suffer from the loss of the multipath diversity by converting frequency-selective fading channels into parallel frequency-flat fading subchannels. To exploit the multipath diversity and reduce the decoding complexity, GLCP OFDM systems with I/Q imbalance are studied. By judiciously assigning the mirror-subcarrier pair into one group, a new encoding/decoding scheme with a low-complexity is proposed to mitigate I/Q imbalance for GLCP OFDM systems in Chapter 5. Since OFDM communication systems have high peak-to-average power ratio (PAPR) problem and are sensitive to carrier frequency offset (CFO), to achieve both the spatial and multipath diversity, time-reversal STBC (TR-STBC) communication systems are introduced. In Chapter 6, the I/Q imbalance mitigating solutions in TR-STBC systems, both in the time domain and in the frequency domain, are studied

DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models

The work identifies the first general, explicit, and non-random MIMO encoder-decoder structures that guarantee optimality with respect to the diversity-multiplexing tradeoff (DMT), without employing a computationally expensive maximum-likelihood (ML) receiver. Specifically, the work establishes the DMT optimality of a class of regularized lattice decoders, and more importantly the DMT optimality of their lattice-reduction (LR)-aided linear counterparts. The results hold for all channel statistics, for all channel dimensions, and most interestingly, irrespective of the particular lattice-code applied. As a special case, it is established that the LLL-based LR-aided linear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimal decoding of any lattice code at a worst-case complexity that grows at most linearly in the data rate. This represents a fundamental reduction in the decoding complexity when compared to ML decoding whose complexity is generally exponential in rate. The results' generality lends them applicable to a plethora of pertinent communication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI, cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimality of the LR-aided linear decoder is guaranteed. The adopted approach yields insight, and motivates further study, into joint transceiver designs with an improved SNR gap to ML decoding.Comment: 16 pages, 1 figure (3 subfigures), submitted to the IEEE Transactions on Information Theor

Adaptive Differential Feedback in Time-Varying Multiuser MIMO Channels

In the context of a time-varying multiuser multiple-input-multiple-output (MIMO) system, we design recursive least squares based adaptive predictors and differential quantizers to minimize the sum mean squared error of the overall system. Using the fact that the scalar entries of the left singular matrix of a Gaussian MIMO channel becomes almost Gaussian distributed even for a small number of transmit antennas, we perform adaptive differential quantization of the relevant singular matrix entries. Compared to the algorithms in the existing differential feedback literature, our proposed quantizer provides three advantages: first, the controller parameters are flexible enough to adapt themselves to different vehicle speeds; second, the model is backward adaptive i.e., the base station and receiver can agree upon the predictor and variance estimator coefficients without explicit exchange of the parameters; third, it can accurately model the system even when the correlation between two successive channel samples becomes as low as 0.05. Our simulation results show that our proposed method can reduce the required feedback by several kilobits per second for vehicle speeds up to 20 km/h (channel tracker) and 10 km/h (singular vector tracker). The proposed system also outperforms a fixed quantizer, with same feedback overhead, in terms of bit error rate up to 30 km/h.Comment: IEEE 22nd International Conference on Personal, Indoor and Mobile Radio Communications (2011

Exploiting Diversity in Broadband Wireless Relay Networks

Fading is one of the most fundamental impairments to wireless communications. The standard approach to combating fading is by adding redundancy - or diversity - to help increase coverage and transmission speed. Motivated by the results in multiple-input multiple-output technologies, which are usually used at base stations or access points, cooperation commutation has been proposed to improve the performance of wireless networks which consist of low-cost single antenna devices. While the majority of the research in cooperative communication focuses on flat fading for its simplicity and easy analysis, in practice the underlying channels in broadband wireless communication systems such as cellular systems (UMTS/LTE) are more likely to exhibit frequency selective fading. In this dissertation, we consider a frequency selective fading channel model and explore distributed diversity techniques in broadband wireless relay networks, with consideration to practical issues such as channel estimation and complexity-performance tradeoffs. We first study a system model with one source, one destination and multiple decode-and-forward (DF) relays which share a single channel orthogonal to the source. We derive the diversity-multiplexing tradeoff (DMT) for several relaying strategies: best relay selection, random relay selection, and the case when all decoding relays participate. The best relay selection method selects the relay in the decoding set with the largest sum-squared relay-to-destination channel coefficients. This scheme can achieve the optimal DMT of the system at the expense of higher complexity, compared to the other two relaying strategies which do not always exploit the spatial diversity offered by the relays. Different from flat fading, we find special cases when the three relaying strategies have the same DMT. We further present a transceiver design and prove it can achieve the optimal DMT asymptotically. Monte Carlo simulations are presented to corroborate the theoretical analysis. We provide a detailed performance comparison of the three relaying strategies in channels encountered in practice. The work has been extended to systems with multiple amplify-and-forward relays. We propose two relay selection schemes with maximum likelihood sequential estimator and linear zero- forcing equalization at the destination respectively and both schemes can asymptotically achieve the optimal DMT. We next extend the results in the two-hop network, as previously studied, to multi-hop networks. In particular, we consider the routing problem in clustered multi-hop DF relay networks since clustered multi-hop wireless networks have attracted significant attention for their robustness to fading, hierarchical structure, and ability to exploit the broadcast nature of the wireless channel. We propose an opportunistic routing (or relay selection) algorithm for such networks. In contrast to the majority of existing approaches to routing in clustered networks, our algorithm only requires channel state information in the final hop, which is shown to be essential for reaping the diversity offered by the channel. In addition to exploiting the available diversity, our simple cross-layer algorithm has the flexibility to satisfy an additional routing objective such as maximization of network lifetime. We demonstrate through analysis and simulation that our proposed routing algorithm attains full diversity under certain conditions on the cluster sizes, and its diversity is equal to the diversity of more complicated approaches that require full channel state information. The final part of this dissertation considers channel estimation in relay networks. Channel state information is vital for exploiting diversity in cooperative networks. The existing literature on cooperative channel estimation assumes that block lengths are long and that channel estimation takes place within a fading block. However, if the forwarding delay needs to be reduced, short block lengths are preferred, and adaptive estimation through multiple blocks is required. In particular, we consider estimating the relay-to-destination channel in DF relay systems for which the presence of forwarded information is probabilistic since it is unknown whether the relay participates in the forwarding phase. A detector is used so that the update of the least mean square channel estimate is made only when the detector decides the presence of training data. We use the generalized likelihood ratio test and focus on the detector threshold for deciding whether the training sequence is present. We also propose a heuristic objective function which leads to a proper threshold to improve the convergence speed and reduce the estimation error. Extensive numerical results show the superior performance of using this threshold as opposed to fixed thresholds

Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little inter-user interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and inter-user interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/massive-MIMO-hardware-impairment

On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin