This paper addresses channel estimation and data equalization on frequency-selective 1-bit quantized Multiple Input-Multiple Output (MIMO) systems. No joint processing or Channel State Information is assumed at the transmitter, and therefore our findings are also applicable to the uplink of Multi-User MIMO systems. System models for both Orthogonal Division Frequency Multiplexing (OFDM) and single-carrier schemes are developed. A Cramér-Rao Lower Bound for the estimation problems is derived. The two nonlinear algorithms Expectation Maximization (EM) and Generalized Approximate Message Passing (GAMP) are adapted to the problems, and a linear method based on the Bussgang theorem is proposed. In the OFDM case, the linear method enables subcarrier-wise estimation, greatly reducing computational complexity. Simulations are carried out to compare the algorithms with different settings. The results turn out to be close to the Cramér-Rao bound in the low Signal to Noise Ratio (SNR) region. The OFDM setting is more suitable for the nonlinear algorithms, and that the linear methods incur a performance loss with respect to the nonlinear approaches. In the relevant low and medium SNR regions, the loss amounts to 2-3 dB and might well be justified in exchange for the reduced computational effort, especially in Massive MIMO settings.
In diffusion-based communication, as for molecular systems, the achievable data rate is low due to the stochastic nature of diffusion which exhibits a severe inter-symbol-interference (ISI). Multiple-Input Multiple-Output (MIMO) multiplexing improves the data rate at the expense of an inter-link interference (ILI). This paper investigates training-based channel estimation schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization methods. Maximum likelihood and least-squares estimators of mean channel are derived, and the training sequence is designed to minimize the mean squar…
The use of low resolution Analog to Digital Converters (ADCs) can significantly reduce the power consumption for massive Multiple Input Multiple Output (MIMO) systems. The existing literature on quantized massive MIMO systems deals with Cyclic Prefix (CP) transmission schemes in frequency-selective fading channels. In this paper, we propose a block processing Frequency Domain Equalization (FDE) technique in CP-free transmission schemes for massive MIMO systems having low resolution ADCs. The optimal block length for FDE is found by minimizing a computational complexity cost function and taking…
Application of filter bank multicarrier (FBMC) as an effective method for signaling over massive MIMO channels has been recently proposed. This paper further expands the application of FBMC to massive MIMO by applying frequency spreading equalization (FSE) to these channels. FSE allows us to achieve a more accurate equalization. Hence, higher number of bits per symbol can be transmitted and the bandwidth of each subcarrier can be widened. Widening the bandwidth of each subcarrier leads to (i) higher bandwidth efficiency; (ii) lower complexity; (iii) lower sensitivity to carrier frequency offse…
We propose equalization-based data detection algorithms for all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-out (MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce complexity. We provide a condition on the number of users, basestation antennas, and channel sparsity for which beamspace equalization can be less complex than conventional antenna-domain processing. We evaluate the performance-complexity trade-offs of existing and new beamspace equalization algorithms using simulations with realistic mmWave channel models. Our results reveal that…
Next-generation wireless systems are expected to combine millimeter-wave (mmWave) and massive multi-user multiple-input multiple-output (MU-MIMO) technologies to deliver high data-rates. These technologies require the basestations (BSs) to process high-dimensional data at extreme rates, which results in high power dissipation and system costs. Finite-alphabet equalization has been proposed recently to reduce the power consumption and silicon area of uplink spatial equalization circuitry at the BS by coarsely quantizing the equalization matrix. In this work, we improve upon finite-alphabet equa…
Orthogonal signal-division multiplexing (OSDM) is an attractive alternative to conventional orthogonal frequency-division multiplexing (OFDM) due to its enhanced ability in peak-to-average power ratio (PAPR) reduction. Combining OSDM with multiple-input multiple-output (MIMO) signaling has the potential to achieve high spectral and power efficiency. However, a direct channel equalization in this case incurs a cubic complexity, which may be expensive for practical use. To solve the problem, low-complexity per-vector and block equalization algorithms of MIMO-OSDM are proposed in this paper for t…
We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naive quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equaliz…
We perform an asymptotic study of the performance of filter bank multicarrier (FBMC) in the context of massive multi-input multi-output (MIMO). We show that the effects of channel distortions, i.e., intersymbol interference and intercarrier interference, do not vanish as the base station (BS) array size increases. As a result, the signal-to-interference-plus-noise ratio (SINR) cannot grow unboundedly by increasing the number of BS antennas, and is upper bounded by a certain deterministic value. We show that this phenomenon is a result of the correlation between the multi-antenna combining tap …
Massive multi-user (MU) multiple-input multiple-output (MIMO) promises significant gains in spectral efficiency compared to traditional, small-scale MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or minimum mean-square error (MMSE)-based methods, typically rely on centralized processing at the base station (BS), which results in (i) excessively high interconnect and chip input/output data rates, and (ii) high computational complexity. In this paper, we investigate the achievable rates of decentralized equalization that mitigates both of these issues. We consider two…
This paper addresses the problem of data detection for a massive Multiple-Input-Multiple-Output (MIMO) base station which utilizes 1-bit Analog-to-Digital Converters (ADCs) for quantizing the uplink signal. The existing literature on quantized massive MIMO systems deals with Cyclic Prefix (CP) transmission over frequency-selective channels. In this paper, we propose a computationally efficient block processing equalizer based on the Expectation Maximization (EM) algorithm in CP-free transmission for 1-bit quantized systems. We investigate the optimal block length and overlapping factor in rela…
Linear data-detection algorithms that build on zero forcing (ZF) or linear minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral efficiency in massive multi-user multiple-input multiple-output (MU-MIMO) systems. Such algorithms, however, typically rely on centralized processing at the base-station (BS) which results in (i) excessive interconnect and chip input/output (I/O) data rates and (ii) high computational complexity. Decentralized baseband processing (DBP) partitions the BS antenna array into independent clusters that are associated with separate radio-frequency c…
This paper deals with linear equalization in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. We first provide simple conditions on the antenna configuration for which the well-known linear minimum mean-square error (L-MMSE) equalizer provides near-optimal spectral efficiency, and we analyze its performance in the presence of parameter mismatches in the signal and/or noise powers. We then propose a novel, optimally-tuned NOnParametric Equalizer (NOPE) for massive MU-MIMO systems, which avoids knowledge of the transmit signal and noise powers altogether. We show tha…
The focus of this letter is on the reduction of the large pilot overhead in orthogonal frequency division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) systems. We propose a novel joint channel estimation and equalization technique that requires only one pilot subcarrier, reducing the pilot overhead by orders of magnitude. We take advantage of the coherent bandwidth spanning over multiple subcarrier bands. This allows for a band of subcarriers to be equalized with the channel frequency response (CFR) at a single subcarrier. Subsequently, the detected data symbols are …
Linear minimum mean-square error (L-MMSE) equalization is among the most popular methods for data detection in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. While L-MMSE equalization enables near-optimal spectral efficiency, accurate knowledge of the signal and noise powers is necessary. Furthermore, corresponding VLSI designs must solve linear systems of equations, which requires high arithmetic precision, exhibits stringent data dependencies, and results in high circuit complexity. This paper proposes the first VLSI design of the NOnParametric Equalizer (NOPE)…
This paper studies the possibility of eliminating the redundant cyclic prefix (CP) of orthogonal frequency division multiplexing (OFDM) in massive multiple-input multiple-output systems. The absence of CP increases the bandwidth efficiency in expense of intersymbol interference (ISI) and intercarrier interference (ICI). It is known that in massive MIMO, different types of interference fade away as the number of base station (BS) antennas tends to infinity. In this paper, we investigate if the channel distortions in the absence of CP are averaged out in the large antenna regime. To this end, we…
This paper presents results on the achievable spectral efficiency and on the energy efficiency for a wireless multiple-input-multiple-output (MIMO) link operating at millimeter wave frequencies (mmWave) in a typical 5G scenario. Two different single-carrier modem schemes are considered, i.e., a traditional modulation scheme with linear equalization at the receiver, and a single-carrier modulation with cyclic prefix, frequency-domain equalization and FFT-based processing at the receiver; these two schemes are compared with a conventional MIMO-OFDM transceiver structure. Our analysis jointly tak…
All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters …
Link-adaptation (LA) is one of the most important aspects of wireless communications where the modulation and coding scheme (MCS) used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a single-user SISO (SU-SISO) system with out-of-cell interference, LA is performed by computing the post-equalization signal-to-interference-noise ratio (SINR) at the receiver. The same technique can be employed in multi-user MIMO (MU-MIMO) receivers that use linear detectors. Another important use of post-equalization SINR is for physical layer (PHY) abstra…
Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimi…
Reconfigurable intelligent surface (RIS)-empowered communications is on the rise and is a promising technology envisioned to aid in 6G and beyond wireless communication networks. RISs can manipulate impinging waves through their electromagnetic elements enabling some sort of control over the wireless channel. In this paper, the potential of RIS technology is explored to perform a sort of virtual equalization over-the-air for frequency-selective channels whereas, equalization is generally conducted at either the transmitter or receiver in conventional communication systems. Specifically, with t…
This letter proposes the Faster-than-Nyquist signaling (FTNS) using overlap frequency domain equalization (FDE), which compensates the inter-symbol interference (ISI) due to band limiting filters of the FTNS at the transmitter and the receiver as well as the frequency selective fading channel. Since overlap FDE does not require any guard interval (GI) at the transmitter such as cyclic prefix (CP), higher spectral efficiency can be achieved compared to FTNS scheme using the conventional FDE. In the proposed method, the equalizer weight is derived based on minimum mean square error (MMSE) criter…
An open-loop single-user multiple-input multiple-output communication scheme is considered where a transmitter, equipped with multiple antennas, encodes the data into independent streams all taken from the same linear code. The coded streams are then linearly precoded using the encoding matrix of a perfect linear dispersion space-time code. At the receiver side, integer-forcing equalization is applied, followed by standard single-stream decoding. It is shown that this communication architecture achieves the capacity of any Gaussian multiple-input multiple-output channel up to a gap that depend…
With the increase of physical antenna and subcarrier numbers in MIMO-OFDM systems, channel side information feedback amount and signal precoding complexity overburden will consume much more system resource, even become intolerable. To solve this problem, previous works mainly focused on fixed subcarrier grouping size and precoded MIMO signals in the same group with unitary channel state information (CSI). It could reduce the system overburden, but such a process would lead to system capacity loss due to the channel mismatch in precoding procedure. In this paper, we consider a MIMOOFDM system o…
Hybrid analog/digital precoding allows millimeter wave MIMO systems to leverage large antenna array gains while permitting low cost and power consumption hardware. Most prior work has focused on hybrid precoding for narrow-band mmWave systems. MmWave systems, however, will likely operate on wideband channels with frequency selectivity. Therefore, this paper considers frequency selective hybrid precoding with RF beamforming vectors taken from a quantized codebook. For this system, a low-complexity yet near-optimal greedy algorithm is developed for the design of the hybrid analog/digital precode…
We consider channel estimation within pulse-shaping multicarrier multiple-input multiple-output (MIMO) systems transmitting over doubly selective MIMO channels. This setup includes MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems as a special case. We show that the component channels tend to exhibit an approximate joint group sparsity structure in the delay-Doppler domain. We then develop a compressive channel estimator that exploits this structure for improved performance. The proposed channel estimator uses the methodology of multichannel group sparse compressed sensing, w…
The most relevant linear precoding method for frequency-flat MIMO broadcast channels is block diagonalization (BD) which, under certain conditions, attains the same nonlinear dirty paper coding channel capacity. However, BD is not easily translated to frequency-selective channels, since space-time information is required for transceiver design. In this paper, we demonstrate that BD is feasible in frequency-selective MIMO broadcast channels to eliminate inter-user interference (IUI) if the transmit block length is sufficiently large, and if the number of transmit antennas is greater than the nu…
Channel estimation is useful in millimeter wave (mmWave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics such as mutual information or signal-to-interference-noise (SINR) ratio. At mmWave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel …
Given the high throughput requirement for 5G, merging millimeter wave technologies and multi-user MIMO seems a very promising strategy. As hardware limitations impede to realize a full digital architecture, hybrid MIMO architectures, using digital precoding and phased antenna arrays, are considered a feasible solution to implement multi-user MIMO at millimeter wave. However, real channel propagation and hardware non-idealities can significantly degrade the performance of such systems. Experimenting the new architecture is thus crucial to confirm and to support system design. Nevertheless, hybr…
In this letter, a novel synchronization method for MIMO-OFDM systems is proposed. The new approach has an accurate estimate of both symbol timing and large frequency offest. Simulation results show the excellent robustness of our method in double-selective channel even if the strongest multipath component arrives behind the first path.
Doubly selective (DS) channel estimation in largescale multiple-input multiple-output (MIMO) systems is a challenging problem due to the requirement of unaffordable pilot overheads and prohibitive complexity. In this paper, we propose a novel distributed compressive sensing (DCS) based channel estimation scheme to solve this problem. In the scheme, we introduce the basis expansion model (BEM) to reduce the required channel coefficients and pilot overheads. And due to the common sparsity of all the transmit-receive antenna pairs in delay domain, we estimate the BEM coefficients by considering t…
As a promising technique to meet the drastically growing demand for both high throughput and uniform coverage in the fifth generation (5G) wireless networks, massive multiple-input multiple-output (MIMO) systems have attracted significant attention in recent years. However, in massive MIMO systems, as the density of mobile users (MUs) increases, conventional uplink training methods will incur prohibitively high training overhead, which is proportional to the number of MUs. In this paper, we propose a selective uplink training method for massive MIMO systems, where in each channel block only pa…
Millimeter wave (mm-wave) and massive MIMO have been proposed for next generation wireless systems. However, there are many open problems for the implementation of those technologies. In particular, beamforming is necessary in mm-wave systems in order to counter high propagation losses. However, conventional beamsteering is not always appropriate in rich scattering multipath channels with frequency selective fading, such as those found in indoor environments. In this context, time-reversal (TR) is considered a promising beamforming technique for such mm-wave massive MIMO systems. In this paper…
Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique, which has the potential to provide significant spectral efficiency improvement. This paper studies the capacity of FTN signaling for both frequency-flat and for frequency-selective multiple-input multiple-output (MIMO) channels. We show that precoding in time and waterfilling in space is capacity achieving for frequency-flat MIMO FTN. For frequency-selective fading, joint waterfilling in time, space and frequency is required.
In this paper, the performance of adaptive turbo equalization for nonlinearity compensation (NLC) is investigated. A turbo equalization scheme is proposed where a recursive least-squares (RLS) algorithm is used as an adaptive channel estimator to track the time-varying intersymbol interference (ISI) coefficients associated with inter-channel nonlinear interference (NLI) model. The estimated channel coefficients are used by a MIMO 2x2 soft-input soft-output (SISO) linear minimum mean square error (LMMSE) equalizer to compensate for the time-varying ISI. The SISO LMMSE equalizer and the SISO for…
