This paper presents a novel wireless autoencoder (WAE) consisting of neural network (NN) modulator and probabilistic data association detector with the aid of deep unfolding (PDA-DU) in coded multi-input multi-output (MIMO) systems. When using error correction code (ECC), the signal constellation that maximizes the minimum Euclidean distance, such as quadrature amplitude modulation (QAM), is not always optimal. For example, set partitioning is often utilized in higher-order modulation schemes. On the other hand, nonlinear iterative signal detection of PDA has been shown to efficiently suppress interference and to reduce the bit error rate (BER) compared to traditional minimum mean square error (MMSE) spatial filtering. However, PDA may degrade detection performance due to outliers in the expected value of transmitted signals. To solve these complicated issues, the proposed WAE retrieves the best signal constellation using the NN modulator, and simultaneously mitigates outliers in the expected value by introducing scaling factors optimized by DU in the PDA detector.

Global navigation satellite system (GNSS) positioning, including the global positioning system (GPS), is susceptible to multipath effects in urban areas and between tall buildings, which degrade positioning accuracy. In this study, to address this issue, we complement GNSS positioning with information obtained through communication with LTE base stations and use it as features for position estimation. Using this information, we propose a long short-term memory (LSTM)-based neural network (NN) model with a parallel structure. In this model, GNSS positioning information and long term evolution (LTE) signal information are combined and input into LSTM with different structures. Features obtained from multiple information sources are used in a complementary manner to improve the accuracy of position estimation. Furthermore, the positioning accuracy of test data is evaluated to confirm the effectiveness of the proposed method. This evaluation takes into account positioning at unknown locations that are different from those used during training.

Generalized approximate message passing (GAMP) achieves near-optimal performance in detecting spatially multiplexed massive multiple-input multiple-output (MIMO) signals with significantly reduced computational complexity under independent and identically distributed (i.i.d.) measurements. However, in spatially correlated MIMO channels where the ideal assumption of large-scale uncorrelated observation does not hold, the detection capability severely deteriorates. This performance degradation can be compensated for by optimizing GAMP with embedded learnable parameters via deep unfolding (DU) techniques, i.e., data-driven tuning; however, the learning process becomes quite unstable. To address this issue, we propose a novel method that achieves stable learning by incorporating a monotonic increase constraint on the reliability of propagated messages by learning the differential (incremental) values of the learnable parameters between two consecutive iterations. The efficacy of the proposed method is confirmed through numerical results in terms of loss trajectory in the learning process and bit error rate (BER) of massive MIMO detection.

This paper proposes a novel vector similarity search (VSS)-based modulation and coding scheme (MCS) selection for massive multi-user multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) systems that employ uplink multi-user detection (MUD) based on iterative signal estimation. To maximize the uplink throughput of MU-MIMO-OFDM systems, it is necessary to assign a carefully selected MCS to each user according to an accurate prediction of the mutual information (MI) that can be achieved with MUD based on the knowledge of the estimated channel state information (CSI). However, since the detection accuracy of iterative MUDs, such as expectation propagation (EP), varies depending on the convergence characteristics, it is challenging to analytically predict the achievable MI in the presence of MUD. To address this difficulty, we propose a novel method for predicting the achievable MI by creating a vector database (VDB) offline that stores feature vectors (keys) computed from CSI and the actual MI (values) achieved with iterative MUD, and then searching this VDB online using approximate nearest neighbors (ANN) search, which enables VSS at ultra-high speed. Simulation results show that the MCS selection based on the proposed MI prediction achieves higher uplink throughput than the conventional schemes in MU-MIMO-OFDM systems using the EP-based MUD.

In this paper, we propose a novel low-complexity Bayesian receiver design to jointly perform channel, CFO, and data estimation from observations subject to different CFO among users in MU-MIMO-OFDM systems. ICI due to CFO significantly reduces channel estimation accuracy under frequency-selective fading environments, making reliable communications difficult. To tackle this difficulty, a JCCE algorithm is designed based on BP. Our method uses a BG distribution as the prior distribution of the channel coefficient to capture its delay-domain sparsity, and a GM distribution as the prior distribution of the phase shift due to CFO to perform parallel search for the allowable range of CFO defined in the 3GPP standard by the number of mixture components. The proposed algorithm can further improve the accuracy of channel, CFO, and data estimation by treating the tentatively detected data symbols as extra pilots. The efficacy of the proposed method is confirmed by numerical studies, which show that the proposed method not only significantly outperforms the SotA methods with much lower computational cost but also approaches the performance of an idealized Genie-aided scheme.

In high-speed mobile communication environments, an orthogonal time frequency space (OTFS) scheme with robustness to doubly-selective fading channels by spreading symbols in the frequency-time (FT) domain has attracted much attention. However, typical pilot-based channel estimation schemes cause system performance degradation due to the increased overhead of channel state information (CSI) acquisition, and large-scale matrix operations based on the size of OTFS equivalent channels are also problematic in terms of the computational cost. To address this issue, in this paper, we focus on the fact that joint channel and data estimation (JCDE) in the delay-Doppler (DD) domain OTFS systems can be formulated as a large-scale parametric bilinear inference problem, and solve it via Gaussian belief propagation (GaBP) to design a novel low-complexity and high-accuracy JCDE algorithm with the use of relatively short pilot sequences. From computer simulations, we confirm that the proposed method significantly outperforms the conventional two-stage channel and data estimation, and asymptotically approaches the idealized scheme given perfect CSI knowledge.

This paper proposes a novel Bayesian receiver design for uplink signal detection in overloaded massive multi-user multi-input multi-output (MU-MIMO) systems. Iterative detection schemes are roughly classified into two types: iterative detection and decoding (IDD), in which log-likelihood ratios (LLRs) are exchanged between the detector and the channel decoder, and self-iterative detection (SID), in which a message passing algorithm (MPA) is operated without involving the channel decoder. In either scheme, the most vital mechanism of iterative signal detection is to decouple the self-feedback across iterations and propagate extrinsic values. As a receiver design to achieve an excellent performance-complexity trade-off while adhering to this mechanism, a concatenated structure of SID based on expectation propagation (EP) and IDD based on turbo equalization is proposed. Numerical simulations show that the proposed method outperforms the state-of-the-art alternatives in terms of bit error rate (BER) performance in coded systems, especially when the overloading ratio is high. In addition, the iterative behavior is analyzed by extrinsic information transfer (EXIT) chart to verify the efficacy of extrinsic value exchange.

This paper proposes deep unfolding (DU)-aided blind signal separation (BSS) for short data block communications of multi-user multi-input multi-output (MU-MIMO) transmission. Independent component analysis (ICA) is a BSS technique that does not require a pilot signal for channel estimation. However, when applying ICA to short data length, inaccurate non-Gaussianity evaluations deteriorate the performance of BSS. To compensate for this drawback, we design a novel DU-aided BSS algorithm as trainable ICA (TICA) that incorporates a data-driven design based on deep learning and learning optimization of internal trainable parameters. Considering that the weight matrix obtained as the outcome of ICA does not converge to an appropriate value for short data length, we introduce two trainable parameters: one for correcting the non-Gaussian evaluation and the other for damping to suppress oscillations. Numerical results show that the proposed algorithm substantially improves the bit error rate (BER) performance with trainable parameter optimization using DU techniques.

This paper proposes a novel precoder design for time-varying millimeter-wave (mmWave) multiple-input multiple-output (MIMO) signal detection based on expectation propagation (EP). In mmWave wireless communications, millisecond-scale fluctuations in the surrounding environment cause significant changes in the wireless channel, and the channel aging over time causes a mismatch between the beam and actual channel. The proposed precoder attempts to maximize detection performance by adjusting information propagated across iterations when employing the EP-based iterative algorithm to suppress interference components due to channel aging at the receiver. Simulation results show that the precoders that impose heterogeneity in quality among streams of MIMO spatial multiplexing minimize the bit error rate (BER). We conjecture that this phenomenon is due to the improved detection accuracy in the early iterations caused by the heterogeneity among streams, and discuss appropriate precoder design criteria by quantitatively evaluating the performance improvement.

In this paper, considering its application in Bluetooth low energy (BLE), we propose a novel self-interference (SI) cancellation method for in-band full-duplex (IBFD) communications in the presence of fractional delay paths. Traditional SI cancellation methods, such as the active SI canceller (ASIC), generate and subtract a replica of the SI signal from the received signal. However, generating this replica requires accurate channel state information (CSI), which increases communication overhead and reduces spectral efficiency. Therefore, the differential active SI canceller (DASIC) has been proposed, which does not require prior CSI information. Nonetheless, the presence of fractional delay paths results in residual SI, making cancellation insufficient. In this paper, we propose a method to generate a replica of the residual SI from DASIC outputs, resolving the residual SI issue. The efficacy of the proposed method is confirmed through computer simulations by comparing its bit error rate (BER) performance with that of ASIC.

This paper proposes an outer loop link adaptation (OLLA) algorithm for massive multi-user multi-input multi-output (MIMO) systems that employs uplink multi-user detection (MUD) based on generalized approximate message passing (GAMP). The contribution aims to improve uplink system throughput performance for future beyond-fifth-generation mo-bile communication systems by designing a novel scheduler that can select spatially multiplexed user equipment (UE) devices and their modulation and coding schemes (MCSs), considering the high detection accuracy provided by the GAMP-based MUD. To achieve this, we propose an OLLA algorithm that accu-rately predicts the signal-to-interference and noise power ratio (SINR) that each UE can achieve after the GAMP-based MUD. Specifically, the proposed method can dynamically optimize the scheduler according to the iterative detection characteristics of GAMP by introducing a mechanism to correct the predicted SINR separately for each combination of spatially multiplexed UEs. System-level simulation results indicate that adjusting our OLLA algorithm achieves a 50% higher throughput than the conventional OLLA algorithm when applied to GAMP.

We propose a novel joint channel and data estimation (JCDE) algorithm via bilinear Gaussian belief propagation (BiGaBP) for massive multi-user MIMO (MU-MIMO) systems with non-orthogonal pilot sequences. The contribution aims to reduce significantly the communication overhead required for channel acquisition by enabling the use of short non-orthogonal pilots, while maintaining multi-user detection (MUD) capability. Bilinear generalized approximate message passing (BiGAMP), which is systematically derived by extending approximate message passing (AMP) to the bilinear inference problem (BIP), provides computationally efficient approximate implementations of large-scale JCDE via sum-product algorithm (SPA); however, as the pilot length decreases, the estimation accuracy is severely degraded. To tackle this issue, the proposed BiGaBP algorithm generalizes BiGAMP by relaxing its dependence on the large-system limit approximation and leveraging the belief propagation (BP) concept. In addition, a novel belief scaling method complying with the data detection accuracy for each iteration step is designed to avoid the divergence behavior of iterative estimation in the early iterations due to the use of non-orthogonal pilots, especially in insufficient large-system conditions. Simulation results show that the proposed method outperforms the state-of-the-art schemes and approaches the performance of idealized (genie-aided) scheme in terms of mean square error (MSE) and bit error rate (BER) performances.

We propose a novel joint channel tracking and data detection (JCTDD) scheme to combat the channel aging phenomenon typical of millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication systems in high-mobility scenarios. The contribution aims to significantly reduce the communication overhead required to estimate time-varying mmWave channels by leveraging a Bayesian message passing framework based on Gaussian approximation, to jointly perform channel tracking (CT) and data detection (DD). The proposed method can be interpreted as an extension of the Kalman filter-based two-stage tracking mechanism to a Bayesian bilinear inference (BBI)-based joint channel and data estimation (JCDE) framework, featuring the ability to predict future channel state information (CSI) from both reference and payload signals by using an auto-regressive (AR) model describing the time variability of mmWave channel as a state transition model in a bilinear inference algorithm. The resulting JCTDD scheme allows us to track the symbol-by-symbol time variation of channels without embedding additional pilots, leaving any added redundancy to be exploited for channel coding, dramatically improving system performance. The efficacy of the proposed method is confirmed by computer simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also approaches the performance of an idealized Genie-aided scheme.

We propose a wireless localization algorithm based on complex-domain super multidimensional scaling (CD-SMDS) augmented with a self-localization (SL) component, whereby each target tracks its own motion by incorporating bearing information, obtained e.g., from integrated inertial sensors. The proposed method improves localization accuracy by simultaneously using the time series information of distance and angle associated to the SL information in order to construct the SMDS rank-one edge kernel matrix, maximizing the noise reduction effect of the low-rank truncation via singular value decomposition (SVD). The efficacy of the proposed method over the original CD-SMDS is confirmed via software simulations, and compared with an SL-aware Cramér-Rao lower bound (CRLB).

This paper proposes a novel angle-of-arrival (AoA) estimation-aided Bayesian joint channel and data estimation (JCDE) algorithm for uplink signal detection in millimeter-wave (mmWave) massive multi-user MIMO (MU-MIMO) systems with short non-orthogonal pilots. In the proposed method, the prior distribution of mmWave channels in the angular domain after digital beamforming is approximated by a Bernoulli-Gaussian (BG) distribution, and the mismatch with the actual distribution is corrected based on AoA estimation by low-complexity angle rotation (AR) method. The resultant beam-domain JCDE algorithm has an inherent mechanism to update path gains of the channels estimated based on AoA for every iteration. This receiver design allows us to take the advantage of both stochastic (i.e., Bayesian) and deterministic (i.e., AR) approaches. Efficacy of the proposed method over the state-of-the-art is confirmed via computer simulations in terms of bit error rate (BER) performance compared to the state-of-the-art alternatives.

This paper proposes a novel blind self-interference (SI) canceller with adaptive differential delay for in-band full duplex (IBFD) communications, even in the presence of a fractional delay path. As an SI cancellation method, active SI canceller (ASIC) that generates and subtracts a replica of the SI signal from the received signal has been proposed. However, the replica generation requires accurate and periodic channel state information (CSI) acquisition, causing system performance degradation due to increased communication overhead. To avoid this inconvenience, differential active SI canceller (DASIC) without prior channel estimation of SI has been proposed. However, due to the effects of a fractional delay path, DASIC cannot completely cancel SI, resulting in residual SI. The proposed method adaptively controls the differential delay to eliminate the residual SI, according to the occurrence pattern of its own transmission signal. In addition, an appropriate maximum a-posteriori probability (MAP) detector is designed based on the DASIC outputs. Finally, computer simulations confirm the efficacy of the proposed method with and without error correction code (ECC) in terms of bit error rate (BER) performance.

We propose a novel joint channel and data estimation (JCDE) scheme to combat the rate limitation in fronthaul links of cell-free massive MIMO (CF-mMIMO) systems introduced by the use of analog-to-digital converters (ADCs) at access points (APs), which makes channel estimation and multi-user detection at the central AP (CAP) challenging. The latter problem is solved here via the new JCDE scheme which differs from state-of-the-art (SotA) alternatives due to two contributions. The first is the design and incorporation of de-quantization (DQ) step which relies only on scalar Gaussian approximation (SGA) assumptions in conformity with mild central limit theorem (CLT), in contrast to the much harder asymptotic conditions required by the classic bilinear generalized approximate message passing (BiGAMP) algorithm. The second is a modification of bilinear Gaussian belief propagation (BiGaBP), whereby quantized outputs are linearized via the Bussgang decomposition enabling tractable signal processing. The resulting DQ-aided JCDE method achieves both low-complexity and high-accuracy by exploiting both the spatial degrees of freedom (DoF) obtained from, and the observations at the CAP to compensate for the low-resolution distortion introduced by, the distributed APs. The efficacy of the proposed method over the SotA is confirmed via computer simulations.

This paper proposes a look-up table (LUT)-based multi-user detection (MUD) scheme imitating approximate message passing (AMP) for uplink signal detection in large multi-user multi-input multi-output (MU-MIMO) systems. When AMP is implemented with double-precision arithmetic, the increase in power consumption and memory occupation becomes a major obstacle to practical application as the system scale expands. To tackle this issue, we design a novel referential AMP detector composed by hierarchically cascading many small LUTs, where only informative integer-valued messages are exchanged on a factor graph. The quantization thresholds of LUTs are sequentially computed by tracking the discrete distribution of the LUT outputs at each layer to minimize the performance degradation owing to quantization errors. Based on the distribution at each layer, the thresholds are optimized by clustering with the Lloyd-Max algorithm using the initial values given by the k-means++ method. Simulation results demonstrate the efficacy of the proposed method in terms of the bit error rate (BER) performance and memory usage. Notably, we show that the referential AMP is robust to changes in communication environments, and then clarify the reason in terms of the algorithmic structure.

In backscatter communications, data is transmitted with up to 1/1000 of the power consumption, instead of the typical several tens of milliwatts. However, if there are multiple nodes for backscatter communication in the same space, harmonics and sidebands limit the number of multiplexing nodes. Both are suppressed by switching the impedance in multiple steps or generating the signal with opposite phases; however, these methods increase the hardware cost of the backscatter tag. This paper proposes harmonics-controlled frequency division multiple access (HC-FDMA), which allows multiple access interference in backscatter communications without hardware modification. HC-FDMA consists of two modules: a modulation scheme and a channel selection algorithm. The modulation scheme controls the harmonics to an arbitrary channel using ∆Σ modulation. The channel selection algorithm optimizes the frequencies of the signal component, harmonics, and sidebands. Targeting IEEE 802.15.4-compatible backscatter, backscatter communication using HC-FDMA achieves 1.45 times multiplexing compared to the case without HC-FDMA. In addition, the analysis results characterize the capability of HC-FDMA.

This paper deals with uplink multi-user detection (MUD) via generalized approximate message passing (GAMP) in highly correlated large multi-user multi-input multi-output (MUMIMO) systems. The most vital mechanism of GAMP is Onsager correction for decoupling the self-noise feedback of beliefs across iterations; this makes it possible to exchange extrinsic values. First, we show that by introducing the belief scaling method proposed in the context of Gaussian belief propagation (GaBP) for adjusting the convergence speed into GAMP, the Onsager correction works properly even under spatial fading correlation, which significantly improves detection capability. Surprisingly, the performance is much better compared to that of the GaBP with belief scaling, and even asymptotically approaches that of computationally expensive expectation propagation (EP) detectors. Based on the results, we clarify why such a dramatic performance improvement is possible only for GAMP in terms of the suppression mechanism of self-noise feedback.

Linear minimum mean square error (LMMSE) filters are often utilized to achieve low-complexity multi-user detection (MUD) in uplink large multi-input multi-output (MIMO) systems. As the scale and density of MIMO systems grow towards truly massive setups, however, the LMMSE detection requiring high-dimensional matrix inversion operations becomes computationally expensive. As a promising approach to tackle this issue, the local LMMSE (LLMMSE) detector was proposed, where a contiguous block of beams can be selected for each user to construct the reduced beam-domain channels, assuming the use of digital beamforming at a base station (BS). A main issue is the performance degradation according to the angular spread of the received signal, due to the information loss induced by an excessive dimensionality reduction aiming at the computational reduction. To alleviate this issue, this paper proposes to selectively combine the information from the overlapped LLMMSE filters in the log-likelihood ratio (LLR) domain. In addition, this method is extended to probabilistic data association (PDA)-based iterative detection scheme for further enhancement of communication reliability. The efficacy of the proposed methods is demonstrated by simulation results in terms of the bit error rate (BER) performance and the computational cost.

We are developing a radio wave sensing technology focusing on antenna impedance. Since this sensing method uses physical phenomena different from conventional sensing technology, it may be able to complement conventional sensors. In the studies, we investigated a sensing method that covers a range of less than several tens of centimeters by measuring the complex voltage reflectance and confirmed its validity by FDTD analysis. However, the sensor circuit for this method had not yet been developed. In this paper, a prototype sensing circuit corresponding to the proposed sensing method was fabricated and evaluated.

This paper proposes a high reliable demodulation method via phase-noise-aware log-likelihood ratios (LLRs) in millimeter-wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems with higher-order quadrature amplitude modulation (QAM). When using high-order QAM schemes in mmWave wide-band wireless communication systems, the phase noise severely degrades the demodulation performance at the operating point in the high signal-to-noise ratio (SNR) region. Typical two-stage processing, which consists of MIMO signal separation (i.e., multi-user detection (MUD)) and phase noise compensation via LLR calculation, cannot properly solve this inherent problem, because the spatial fading correlation in mmWave channels causes phase noise enhancement during MUD. To circumvent this issue, we formulate an approximate probability density function (PDF) of the received signals including phase noise, which enables us to jointly perform MUD and phase noise compensation; this makes it possible to compute reliable LLRs to be input to the channel decoder, without phase noise enhancement. Numerical results demonstrate the efficacy of the proposed method in terms of the bit error rate (BER) performance in coded mmWave MIMO systems.

We propose two full-digital receive beamforming (BF) methods for low-complexity and high-accuracy uplink signal detection via Gaussian belief propagation (GaBP) at base stations (BSs) adopting massive multi-input multi-output for open radio access network. In such scenarios, it is vital to reduce the cost of the BSs by limiting the bandwidth of fronthaul (FH) links, and the dimensionality reduction of the received signal based on receive BF at a radio unit is a well-known strategy to reduce the amount of data transported via the FH links. We clarify appropriate criteria for designing a BF weight considering the subsequent GaBP signal detection with the proposed methods: singular-value-decomposition-based BF and QR decomposition-based BF with the aid of discrete-Fourier-transformation-based spreading. Both methods enable dimensionality reduction without compromising the desired signal power by taking advantage of a null space of the channels. BF reduces correlations between the received signals in the BF domain, which improves the robustness of GaBP against spatial fading correlation. Simulation results indicate that the proposed methods improve detection capability while significantly reducing computation.

In large multi-user multi-input multi-output systems, the computational cost and circuit scale of base stations (BSs) are effectively reduced using two-stage signal processing consisting of a slow varying outer beamformer (OBF) based on long-term channel statistics and group-specific multi-user detection for instantaneous channel variations. However, the dimensionality reduction of the group-specific beam-domain channel based on the OBF causes significant performance degradation in the subsequent spatial-filtering detection. To compensate for this drawback, this paper introduces a novel layered belief propagation (BP) detector with a concatenated structure of beam- and antenna-domain BP layers for post-stage OBF processing. The proposed detector is designed for improving the convergence of iterative detection by suppressing intra- and inter-group interference in stages. The layered structure provides the advantages of both beam and antenna domains while maintaining low signal-processing complexity. Numerical results show the validity of our proposed method in terms of the bit error rate performance in both the uncoded and coded cases and the computational complexity.

We consider large multi-user detection (MUD) via deep unfolding-aided belief propagation (BP) in coded multi-user MIMO (MU-MIMO) systems. A BP detector optimized (trained) by data-driven-tuning of embedded internal parameters achieves low-complexity and high-accuracy MUD while compensating practical imperfections. However, in actual implementation, these parameters should be optimized according to system parameters, e.g., modulation and coding scheme (MCS). In particular, when channel coding is used, it is vital not only to minimize the mean square error (MSE) but also to enhance the Gaussianity of the output log-likelihood ratio (LLR), in order to maximize the error correction capability of the subsequent soft-decision decoder. To that end, a novel loss function based on a weighted average of negentropy, which is a key measure to evaluate the Gaussianity, and MSE of the detector output is proposed. Simulation results show that the trainable Gaussian BP (T-GaBP) detector optimized with the proposed negentropy-aware loss function significantly improves the bit error rate (BER) performance of the decoder output and substantially outperforms the T-GaBP optimized with the typical MSE loss function.

We propose a novel joint channel and data estimation (JCDE) scheme for highly-correlated large multi-user multi-input multi-output (MIMO) systems with short non-orthogonal pilots. Bayesian JCDE via scalar-wise tensor products is a solid strategy for achieving multi-user detection (MUD) with extremely low computational cost, but the convergence property is significantly degraded in practical MIMO systems assuming spatially correlated fading channels. When using ultra-short pilots aiming at significant overhead reduction, the reliable MUD via JCDE becomes increasingly challenging. To address this issue, the proposed method estimates channel coefficients via a maximum a-posteriori (MAP)-like approach with the aid of long-term channel statistics. Furthermore, while the data detection is performed via probabilistic data association (PDA) to suppress the observation correlation, the channel estimation is performed via Gaussian belief propagation (GaBP) by leveraging the pseudo-orthogonality of the pilot-plus-data sequences, which makes it possible to realize low-complexity and high-accuracy Bayesian JCDE for highly-correlated MUD. Computer simulation demonstrates the validity of our proposed method in terms of bit error rate (BER) performance and computational cost.

This paper proposes bilinear Gaussian belief propagation (BiGaBP) for joint channel and data estimation (JCDE) in large multi-user multi-input multi-output (MU-MIMO) systems. JCDE is a well-known strategy for realizing a high-precision MU detection (MUD) with short pilots by utilizing the orthogonality of data sequences. For massive MIMO scenarios, the JCDE via bilinear generalized approximate message-passing (BiGAMP), which is systematically derived by extending AMP to the bilinear inference problem (BIP), achieves extremely low computational cost. However, the use of short non-orthogonal pilots to reduce the channel acquisition overhead significantly degrades the convergence property of BiGAMP. To resolve the lack of an appropriate JCDE scheme based on insufficient pilots, we design a novel MP rule based on GaBP, which is given by relaxing the large-system approximation from AMP. Furthermore, the belief scaling complying with the detection state in each iteration step is introduced to suppress the negative impact of non-orthogonal pilots even in the insufficient large-system conditions. Numerical results show the validity of our proposed method in terms of bit error rate (BER) and mean square error (MSE) performances.

This paper proposes a deep unfolding-aided belief propagation (BP) for large multi-user multi-input multi-output (MU-MIMO) detection under correlated fading channels. A BP-based detector is a well-known strategy for realizing large-scale MU detection (MUD) with low-complexity and high-accuracy. However, its convergence property is severely degraded under insufficient large-system conditions and spatial fading correlation among RX antenna elements. To compensate for this drawback, we design a trainable Gaussian BP (T-GaBP) having well-organized trainable internal parameters based on the BP structure. These parameters are optimized by the deep learning techniques in the signal-flow graph of unfolded GaBP; this approach is referred to as data-driven tuning. By training the parameters according to the system model, T-GaBP can maintain the high detection capability even in practical system configurations that differ from the ideal uncorrelated massive MIMO assumption. Numerical results show that the proposed detector improves the convergence property and achieves a comparable detection performance to the cutting-edge expectation propagation (EP) detector in correlated MUD, with a lower computational cost.

This paper deals with mmWave overloaded multiuser multi-input multi-output (MU-MIMO) detection, where the number of receive antennas is less than that of transmitted streams. Belief propagation (BP) is well known strategy for achieving large-scale MU detection (MUD) with low-complexity and high-accuracy. However, in mmWave massive MUD, the BP-based signal detector is subject to ill convergence behavior of iterative detection due to under-determined problem induced by spatial overloading and strong correlation among user channels induced by narrow angular spread of receive signal and line-of-sight (LOS) environments. To alleviate these impairments, we propose a novel iterative MUD approach based on beam-domain subspace marginalized BP (SMBP). Exploiting the approximate sparsity of beam-domain channels, the maximum likelihood (ML) principle is used to combine the strongly correlated signal subspace with reduced dimension while the BP-based detection is used for the remaining complementary subspace. The space partitioning criterion is adaptively determined based on channel state information (CSI) so that the two subspaces are as orthogonal as possible. Numerical results show that the proposed method is able to serve a massive number of wireless connections with low computational complexity even in the LOS environment, while providing excellent BER performance.

This paper proposes low-complexity beam-domain channel estimation using long-term channel statistics in belief propagation (BP) based large multi-input multi-output (MIMO) detection. When the channel correlation matrix between the base station (BS) and each user equipment (UE) is available and used as prior information, maximum a-posteriori probability (MAP) estimation provides the optimal estimation performance. However, it requires undesirably complex large-scale matrix operations at any time the channel statistics is changed. By appropriately selecting beam-domain angular bins for each UE, the proposed method allows us to significantly reduce the computational cost while maintaining the near-optimal performance in terms of the mean square error (MSE) of estimated channel. The selection threshold is adaptively determined based on the prior information such as the channel correlation matrix, statistical beam, and receive SNR. For the subsequent BP-based signal detection, an appropriate covariance matrix is designed while considering the detrimental impact of channel estimation errors. Numerical results show that the proposed method can reduce the computational cost to less than 4% as compared to the MAP estimation, while providing similar MSE performance.

This paper proposes a novel layered belief propagation (BP) detector with a concatenated structure of two different BP layers for low-complexity large multi-user multi-input multi-output (MU-MIMO) detection based on statistical beams. To reduce the computational burden and the circuit scale on the base station (BS) side, the two-stage signal processing consisting of slow varying outer beamformer (OBF) and group-specific MU detection (MUD) for fast channel variations is effective. However, the dimensionality reduction of the equivalent channel based on the OBF results in significant performance degradation in subsequent spatial filtering detection. To compensate for the drawback, the proposed layered BP detector, which is designed for improving the detection capability by suppressing the intra- and inter-group interference in stages, is introduced as the poststage processing of the OBF. Finally, we demonstrate the validity of our proposed method in terms of the bit error rate (BER) performance and the computational complexity.

This paper focuses on multi-user detection in uplink Massive multiple-input multiple-output (MIMO) systems. As a low computational complexity signal detection algorithm, matched filter (MF)-based Gaussian belief propagation (MF-GaBP) using adaptively scaled belief (ASB) has been proposed. In addition, to suppress the negative impact of spatial correlation, an MMSE detector is introduced as the prior stage processing of MF-GaBP. Through computer simulations, this algorithm exhibits high performance even in high- spatially loaded Massive MIMO scenarios. However, in actual radio environments, there is a possibility to observe an extremely high channel correlation among some antenna elements or some users due to co-located polarization antenna elements or closely deployed mobile stations (MS). The highly correlated channel results in the performance degradation of MF-GaBP using ASB because of the low reliability of initial beliefs. This paper aims to verify the performances of MF- GaBP in the actual radio environments. The predetermined parameters in MF- GaBP are also adjusted according to actual environments for improving the convergence property of the iterative detection. To obtain propagation channel data for performance evaluation in the actual environments, an indoor experimental trial using Massive MMO equipment with 32 antenna elements was carried out under a condition that 16 MSs are closely deployed. Computer simulations using the obtained propagation channel data validate that simply applying the MMSE detector before MF-GaBP with ASB suppress an error floor in the bit error rate (BER) performance even in highly correlated channel.

This paper proposes a symbol replica generation method using look-up table (LUT) based on the information bottleneck (IB) theory in matched filter Gaussian belief propagation (MF-GaBP) for massive multi-input multi-output (MIMO) detection. MF-GaBP serves as an iterative signal detection scheme with low computational complexity by utilizing massive MIMO simplification owing to the law of large numbers. However, when the internal mathematical processes in MF-GaBP are conducted in double precision, a severe processing delay is inevitable. To avoid the impairment, we propose a quantized MF-GaBP detection scheme using predesigned LUTs. The quantization threshold is designed based on the sequential IB (sIB) method for minimizing the mutual information loss. Finally, computer simulations demonstrate that the proposed method significantly reduces the memory occupancy on the basis of table-based processing while suppressing error floor level of the bit error rate (BER) performance.

This paper proposes an adaptively scaled belief (ASB) in Gaussian belief propagation (GaBP) designed for use in large multi-user multi-input multi-output (MU-MIMO) detection under higher order modulation schemes. In practical MU-MIMO systems, the dominant factor in the poor convergence of GaBP iterative detection is approximation errors arising from the fact that the law of large numbers does not work well in many such applications as a result of physical system limitations. Unfortunately, the approximation errors become more severe when there is higher correlation among typical bit-wise prior beliefs when higher order quadrature amplitude modulation schemes are used. To cope with the impairments arising from inter-bit correlation, symbol-wise beliefs can be defined for GaBP self-iterative detection, although this still does not remove approximation errors. As a simple method for mitigating the harmful effects of approximation error, this paper proposes a novel method for adaptive belief scaling while stabilizing the dynamics of random MIMO channels. Based on the functionality of ASB, we also propose a method for approximately calculating conditional expectations with lower computational complexity without sacrificing detection capability. Finally, the validity of using ASB for symbol-wise iterative detection in suppressing the bit-error-rate floor level is confirmed.

This paper proposes a random codebook design and its constraints for Gaussian belief propagation (GaBP) in massive Sparse Code Multiple Access (SCMA). In typical SCMA, a message passing algorithm (MPA) is applicable as the detection scheme for multi-user detection (MUD) on the assumption of a predefined optimal codebook. As the number of transmit streams increases, however, it becomes quite difficult to design optimal codebook due to a large number of its candidates. To address such an inconvenient problem, we employ the structured random codebook and GaBP iterative detection, which may achieve the near-optimal performance thanks to the diversity gain in the large-scale MUD. Moreover, an abnormal noise enhancement induced in GaBP can be suppressed by appropriate constraints in random codebook design. To improve the convergence property of GaBP iterative detection, we propose a novel random codebook design and its constraints for GaBP. Finally, we demonstrate the validity of our proposed method, in terms of improvement of bit error rate (BER) performances.

This paper proposes a unified design of log likelihood ratio (LLR) quantization and joint reception (JR) to reduce the mobile fronthaul (MFH) bandwidth in the split-physical (PHY) processing (SPP). SPP is one of the schemes that redefine the base station (BS) functional split between a baseband unit (BBU) and a remote radio head (RRH) in cloud radio access network (C-RAN). SPP splits the BS functions between the channel encoder/decoder and the modulator/demodulator of the PHY layer functions. In the uplink scenarios, the RRH forwards demodulator output LLR to the channel decoder at the BBU through the MFH. To reduce the MFH bandwidth, the number of LLR quantization bits must be minimized while maintaining the requested quality after JR, for example in terms of bit error rate (BER) requirement. To deal with the minimization, the proposed scheme adaptively controls the LLR quantization threshold according to the received signal to noise ratio (SNR) as well as the capability of source coding for data compression of the quantized LLR. Numerical simulations show that the proposed scheme is capable of reducing the average number of LLR quantization bits to fewer than 2 bits and 1.1 bits at the minimum with a JR block error rate (BLER) performance superior to that of selection combining.

This paper proposes a successive interference cancellation (SIC) of independent component analysis (ICA) based spatial division multiple access (SDMA) differential detector for Bluetooth low energy (BLE) systems. Typical SDMA scheme requires estimations of channel state information (CSI) using orthogonal pilot sequences. However, the orthogonal pilot is not embedded in the BLE packet. This fact motivates us to apply ICA detector into BLE. In this case, there are three problems to be addressed. First, ICA requires a lot of samples for maintaining the accuracy of signal separation capability. Then, ICA suffers from phase ambiguity. Finally, noise enhancements are caused by ICA detector. To deal with all problems, we propose a unified design of ICA, differential detector, and SIC for SDMA of BLE. Regarding the packet error rate (PER) performance, computer simulations validate the proposed design.

This paper proposes a normalized matched filter (MF) belief in Gaussian belief propagation (GaBP) detection especially for a large multiple-input multiple-output (L-MIMO) configuration where a base station (BS) has tens of antennas. In a massive MIMO channel where the BS has hundreds of antennas, damped GaBP is known to be an effective detector in terms of low computational complexity and its detection capability. However, in L- MIMO channels, GaBP is subject to ill convergence behavior of iterative detection due to lack of channel hardening effects obtained by massive number of receive antennas. To improve the convergence property, we investigate the MF belief, instead of a traditional log likelihood ratio (LLR) belief. Then, we propose the novel normalized MF belief according to instantaneous channel state. As a side effect of the normalization, a noise variance estimator is not necessary. Finally, we demonstrate the validity of the normalized MF belief with the aid of damped processing, in terms of suppression of bit error rate (BER) floor as well as approach to maximum likelihood detection (MLD) limit.

This paper proposes an iterative cancellation scheme for adjacent channel interference (ACI) caused by nonlinear transmit power amplifier (PA) in millimeter wave (mmW) systems with the assistance of a nonlinear estimator and channel decoder's feedback. In this method, nonlinear distortion included in the transmit signal is estimated by polynomial regression analysis using not only the pilot signal but also the regenerated modulated signal in the receiver to enhance estimation accuracy in the higher signal level regions. Frequency selective fading is also compensated for. We have demonstrated that ACI cancellation with proposed nonlinearity estimation is capable of improving bit error rate (BER) performances in the presence of ACI, and have shown effectiveness and usefulness of the proposed scheme when IEEE 802.11ad using spectrum bands located adjacently in the same place.

This paper describes a virtual differentially coded system without a differential encoder at the transmitter and channel estimates at the receiver. Without the assistance of a differential encoder, a differential decoder detects a linearly multiplexed symbol that is constituted by two transmitted symbols in different times. In the virtual differentially coded system, the multiplexed symbol is regarded as a virtual symbol. In order to resolve the virtual symbol into the two original symbols, extrinsic log likelihood ratio (LLR), which is fed back from the channel decoder, is exploited with the aid of iterative decoding. For the sake of improving the convergence property of iterative decoding, sparse pilot symbols are doped in a stream of transmitted symbols. We demonstrate that the proposed system with a small number of sparse pilot tones is very assistive in terms of frame error rate (FER) performance. Furthermore, we explicitly show that the proposed approach outperforms the conventional differential and coherent detection schemes under the condition of a few sparse pilots.

This paper proposes a frequency-domain (FD) scheduling for an uplink single carrier non-orthogonal multiple access (SC-NOMA) with the assistance of iterative interference cancellation (IC). In the SC-NOMA, an evolved Node B (eNB) with an iterative IC receiver is capable of allocating overlapped spectra to plural intra-cell user equipments (UEs) exceeding the number of receive antennas. As a result, the SC-NOMA can enhance the spectral efficiency owing to the non-orthogonal multiplexing. However, when the subband (SB) criterion scheduling, that assigns an SB to a UE on the basis of the metric only on an SB, is applied, this scheduling is not suitable in the SC-NOMA. This is because the throughput in single carrier transmission depends on all the allocated SBs. Therefore, we propose a novel FD scheduling based on the expected value of cell throughput which is calculated by both the SB selected by the metric as a candidate and the previously allocated SBs. The eNB assigns the candidate SB if the expected cell throughput increases after allocating the candidate SB. Computer simulations confirm that the proposal can improve throughput performances compared to the SB criterion scheduling in the SC-NOMA.

This paper proposes a design strategy for decode- quantize-forward (DQF) relaying in which two relay nodes parallelly forward quantized a-posteriori loglikelihood ratio (LLR) after channel decoding. A primary focus is on cross correlation among quantized bits. When the source signal is received by plural relays and they are transmitted independently, the parallel streams can be regarded as an imperfect parallel repetition streams, and the final destination can detect the concatenated code sequences. In the proposed scheme, taking into account this feature, we have designed its iterative unified decoding modules, in which we have a novel iterative unified decoding schemes that consists of vertical and horizontal iterations. We have demonstrated that the iterative unified decoding is capable of obtaining large iterative gain in terms of bit error rate (BER).

This paper proposes a novel extrinsic log likelihood ratio (LLR) of soft demapper to be conveyed to maximum a-posteriori probability (MAP) decoder under the presence of channel estimation errors. In principle, the extrinsic LLR should obey Gaussian distribution for the sake of reducing computational complexity in the MAP decoder. However, channel estimation errors deteriorate Gaussianity of the LLR derived by conventional definition. In order to guarantee the Gaussianity, this paper defines alternative extrinsic LLR according to mean square error (MSE) of channel estimates. The derivation of proposed LLR is extended to an iterative channel estimation scenario. We will demonstrate that the proposed extrinsic LLR is capable of improving bit error rate (BER) performances.

This paper proposes an autonomous grouping in subsystem band for spectrum sharing with the aid of selfish dynamic spectrum control (S-DSC) for asynchronous private wireless networks. When S-DSC, in which each transmitter maps its original spectrum to spectrum with higher channel gain of a system band in a selfish manner, is employed in each link of a shared band, and their transmission timing is asynchronous, there arises mutual interference components in the receiver even at null spectrum positions of the original interference signal, and this causes a so-called near-far problem, which is much severer than the transmit timing synchronous case. To alleviate the critical problem, the proposed scheme introduces an autonomous subsystem band grouping in which each user autonomously selects one of the subsystem band in a system band to maximize its expected average capacity, thereby user links with moderate mutual interference are grouped in the same subsystem band autonomously. Computer simulations confirm that the proposed scheme mitigates the impact of the aforementioned near-far problem. Moreover, the simulations confirm that the frame error rate (FER) performance of the proposed scheme is better than that for the centralized control assumed orthogonal frequency division multiple access (OFDMA).

This paper deals with an adaptive bit-wise switching strategy for hybrid decode-forward (DF) and decode-amplify-forward (DAF) relaying and proposes a novel bit-wise partial noise elimination technique. In relay nodes of DAF relaying, bit log likelihood ratios (LLR)s with high reliability is proactively hard-decoded to cut noise propagation. With this method, we have a difficulty to calculate LLRs in the destination node, due to multi-modality of probability density function (PDF) caused by nonlinear operations of the noise elimination. In order to resolve this issue, a classification problem is formulated in this paper. We will demonstrate that DAF with the bit-wise noise elimination functionality is capable of improving frame error rate (FER) performance in the destination node.

In this paper, we propose a new criterion for the extrinsic information transfer (EXIT) analysis in the spectrum-overlapped resource management (SORM) for a broadband single carrier transmission. In the SORM technique, each user can ideally obtain a maximum channel gain by allowing overlapped allocation, assuming the soft canceller with minimum mean square error (SC/MMSE) turbo equalization for multi-user detection. When the interference is not completely removed by SC/MMSE turbo equalization, frame error rate (FER) remarkably degrades. To solve this problem, we proposed a new criterion which is used for design of error correcting code in order to improve convergence property of SC/MMSE turbo equalization without large redundancy of channel code. In the proposed criterion, we design the decoder so that the decoder's EXIT property is fitted to the upper bound of equalizer's property and the channel code sharply improves mutual information (MI) in the lower amount of input MI. This paper evaluates FER performance with the irregular low-density parity-check (LDPC) codes designed based on this criterion. As a result, this paper shows that the irregular LDPC code designed based on the proposed criterion for the SORM outperforms conventional codes.

This paper presents a novel base station (BS) cooperation scheme to address issues on fatal links experienced by cell-edge users based on bit interleaved repetition coded (BIRC) concatenated structures for downlink OFDM scenarios. A primary focus is on employing independent random interleavers in cooperating BSs following by BS cooperation which may be regarded as the repetition code. In early works related to the BS cooperation, the main paradigm was just for an enhancement of signal levels after a signal combining, in terms of improvements of channel capacity. However, in order to achieve the enhanced channel capacity, we have to design not only appropriate channel codes but also perfect channel state information (CSI) notifications. The proposed simple BIRC based BS cooperation technique has a potential to overcome above mentioned issues through unified design via an iterative fashion. In this paper, high diversity and coding gains of BIRC based BS cooperation are observed in computer simulations, comparing to an Alamouti's STBC based BS cooperation scheme.

This paper presents a relay activation scheme designed for joint channel-and-network (JCN) coded systems relying on an iterative decoding. A primary focus is on proposing criteria of the relay activation to find the best user combination for cooperative relaying, which exploits extrinsic information transfer (EXIT) chart analysis. We will demonstrate that the EXIT chart aided relay activation scheme is capable of reducing the probability of outages, despite increasing the effective throughput of network.

This paper proposes an adaptive bandwidth control scheme for autonomous distributed networks. Based on the consideration that the carrier sense multiple access with collision avoidance (CSMA/CA) is not efficient in terms of throughput due to its strict collision avoidance process, this paper employs a dynamic spectrum control (DSC) in which a certain number of discrete spectra having the highest signal to interference power ratio (SINR) is selected in a selfish manner in each link allowing a partial band interference, and such interference is suppressed by the equalizer in the receiver. For optimal selection of the band activity ratio (BAR) for the selfish DSC in the sense of throughput maximization, this paper also proposes a channel capacity maximization-based BAR control scheme. Computer simulation confirms that the proposed scheme is achieving high throughput efficiency in the autonomous distributed networks.

This paper proposes a cooperative relay transmission exploiting a concatenated structure composed of a simple exclusive OR (XOR) network code and channel codes. A main contribution of this paper is to clarify the joint network-and-channel coding combined with an iterative decoding is effective in providing highly reliable wireless communications even in the case where distances from one destination node to two source nodes are not the same in the case of triangular topology network. In this case, it is subject to imbalanced signal powers at the destination node, resulting in imbalanced link quality. In this paper, we focus on an analogy between the triangular topology network and a quaternary PSK (QPSK) symbol with a rectangular shape, i.e., imbalanced the in-phase and quadrature-phase (I-Q). After demonstrating that the rectangular QPSK has a potential to detect signals with high accuracy by iterative decoding, a configuration of the cooperative relay to be proposed in this paper is revealed. Computer simulation confirms that both the rectangular QPSK and the cooperation are effective to solve the imbalanced problem.

This paper proposes a scheduling and adaptive rate control scheme for multi-user multiple-input multiple-output (MIMO) systems in the uplink, designed to improve system throughput in single-carrier broadband wireless systems exploiting a frequency domain soft canceller with minimum mean square error (FD-SC/MMSE) turbo equalizer. Aiming at the reduction of computational burden involved in the scheduling and adaptive rate control while maintaining high throughput efficiency, scheduling including a stream selection is first conducted in the base station (BS) based on the expected signal to noise power ratio (SNR) under an assumption that the turbo receiver is converged. The BS then conducts a coding rate optimization for each scheduled stream to maximize the throughput efficiency. A main contribution is to reveal a mechanism that the convergence property is controlled to maximize the throughput efficiency by a prediction of the iterative behavior based on extrinsic information transfer (EXIT) trajectory which is predicted from channel transfer functions and a window control proposed in this paper. Computer simulation confirms that the achievable average system throughput can be significantly improved by the proposed scheme.

This paper proposes a spectrum-overlapped resource management (SORM) technique using a dynamic spectrum control (DSC) and turbo equalizer for flexible frequency domain resource management under multiuser conditions. In the proposed technique, discrete spectrum of the transmitted signal is mapped onto spectrum components having the highest channel gain in the system band while accepting partial spectrum overlapping between users, and a frequency domain soft-canceller with minimum mean square error (FD-SC/MMSE) turbo equalizer is applied as the signal detection scheme for multiuser detection (MUD) in order to cancel not only inter-symbol interference (ISI) but also inter-user interference (IUI). Computer simulation including extrinsic information transfer (EXIT) analysis confirms that the proposed scheme can achieve high frequency selection diversity and realize flexible frequency domain resource management.

This paper proposes a relay-assisted re-transmission scheme based on mutual information for wireless mesh networks. In the proposed scheme, re-transmission is done not from the source node but from the relay node, and minimal amount of spectrum corresponding to the remaining uncertainty for the data sequence is re-transmitted, where the number of spectrum components to be fed back is determined based on expected mutual information after re-transmission. Moreover, to directly convert reduction of feedback spectrum to spectrum efficiency improvement in carrier sense multiple access with collision avoidance (CSMA/CA) introduced systems, the re-transmitted spectrum is mapped on whole the bandwidth with equal spacing, thereby the time duration for the re-transmission is reduced. Computer simulation confirms that the proposed scheme can achieve much higher system throughput efficiency especially than the conventional two-hop transmission schemes.

This paper proposes a partial spectrum retransmission scheme using a dynamic spectrum control for broadband single carrier transmission systems. In the proposed scheme, using a dynamic spectrum control in which discrete spectrum of the transmitted signal is mapped onto an arbitrary part of the assigned spectrum, only a certain amount of deeply faded spectrum components are retransmitted via a part of channel having relatively a higher channel gain. The amount of retransmitted components are determined based on mutual information between the transmitted signal and the output of an minimum mean square error (MMSE) based frequency domain equalizer (FDE). Moreover, plural retransmission frames are multiplexed in a retransmitted frame for efficient utilization of the retransmitted spectrum component reduction. Computer simulation confirms that the proposed scheme can achieve high throughput performances even under low terminal mobility or quasi-static conditions.

In this paper, we introduce adaptive link control techniques for multiple-input multiple-output (MIMO) systems with broadband single-carrier signaling. Soft cancellation and minimum mean squared error turbo equalization is assumed, where matching between coding and equalization plays crucial roles to achieve high throughput. This paper uses multilevel coded bit-interleaved coded modulation (ML-BICM) with linear mapping as a core part of the transmission, on top of which this paper applies automatic repeat request (ARQ) with adaptive coding (AC) for link control. The reason behind the use of the linear mapping ML-BICM is the separability between the layers in the modulation format: This should bring us a significant benefit in designing the link control strategies because each layer has its own reliability, and the adaptive link control should be optimized based on the each layer's reliabilities. For ARQ with ML-BICM, we introduce layer-by-layer retransmission control, where turbo equalization and retransmission control can be performed independently over the layers because of the layer-separability. The layer-by-layer concept is then applied to AC with ML-BICM, where the code parameters such as code rate and generator polynomials are chosen so that after several iterations the mutual information between the transmitted and the soft-input soft-output decoder output information can reach a value very close to one while minimizing the rate loss due to the mismatch between the equalization and decoder. Extrinsic information transfer analysis is performed for the MIMO channel realization being given. The transmitter is notified of the selected codes for the each layer via the feedback channel, and uses the selected codes for the following transmission, assuming that the channel state information (CSI) stays the same at least over two consecutive frames. Finally, this paper evaluates the throughput performances of the ML-BICM ARQ and AC techniques using multidimensional field measurement data. The performance tendencies are correlated with propagation properties, obtained as results of the high-resolution channel analysis, such as spatial spreads at the transmitter and receiver sides.

This paper will discuss trend and our views on the flexible spectrum control and receiver performance improvement technologies as a part of capacity maximization technologies for beyond third generation (B3G) systems. First of all, we will discuss dynamic spectrum control techniques that can minimize intra- and inter-system interference for both multi-carrier (MC) and single-carrier (SC) systems, thereby maximizing upper-limit of channel capacity. We will also discuss receiver performance improvement strategies as a part of capacity maximization technologies for MC and SC systems, especially joint operation of the adaptive equalizer and decoder using EXIT chart analysis for MC and SC systems