In frequency division duplex massive multiple-input multiple-output systems, downlink channel state information must be fed back within a limited uplink budget. While transform coding with Karhunen-Loeve transform and reverse water-filling is rate-distortion optimal for Gaussian channels, its performance is limited by basis mismatch between the user and base station. We analyze this mismatch and propose a practical architecture separating long-term basis feedback from short-term coefficient quantization. Using a random vector quantization, we derive a closed-form end-to-end mean square error expression. This allows us to characterize the optimal rate split and identify a phase transition threshold for basis updates. Simulations on correlated Gaussian and COST2100 channels demonstrate near-optimal performance, robustness to update overhead, and significant complexity reduction compared to deep-learning-based autoencoders.

We study channel state information (CSI) compression for wideband frequency division duplex massive multiple-input multiple-output (MIMO) when the base station (BS) reconstructs CSI using an imperfect covariance model. Under matched second-order statistics, remote rate--distortion theory yields transform coding with reverse water-filling (RWF) over covariance eigenmodes. With decoder-side covariance mismatch, however, this allocation is no longer end-to-end optimal. We derive an achievable mismatched Gaussian rate--distortion characterization based on a Gaussian test channel and a mismatched minimum mean square error (MMSE) reconstruction rule. In a shared-eigenvector regime (common eigenbasis, mismatched eigenvalues), the problem decouples across modes and leads to a robust reverse water-filling (RRWF) allocation computable via bisection and per-mode root finding. Simulations using wideband massive MIMO covariance models show that RRWF consistently improves reconstruction distortion and end-to-end mean square error relative to conventional RWF under mismatch.

This paper addresses precoder design for secure multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) systems. We introduce a MIMO channel with a multiple-antenna eavesdropper and a multiple-antenna sensing receiver (MIMO-ME-MS) and analyze the fundamental performance limits of this tripartite tradeoff. Using sensing mutual information, we formulate the precoder design as a nonconvex weighted sum rate maximization problem. A high signal-to-noise ratio analysis based on a subspace decomposition characterizes the maximum weighted degrees of freedom. This analysis reveals the structure of a quasi-optimal precoder that must span the “useful subspace” and demonstrates the inadequacy of extending known schemes from simpler wiretap or ISAC channels. To solve this nonconvex problem, we develop a practical two-stage iterative algorithm that alternates between a sequential basis construction stage and a power allocation stage that solves the resulting difference-of-convex program. We demonstrate that the proposed method captures the desirable precoder structure identified in our analysis and achieves substantial performance gains in the MIMO-ME-MS channel.

We investigate full-duplex (FD) multi-user multiple input single-output systems with coarse quantization, aiming to characterize the impact of employing low-resolution analog-to-digital converters (ADCs) on self-interference (SI) and to develop a quantization- and SI-aware beamforming method that alleviates quantization-induced performance degradation in the FD systems. We first present an analysis on the perantenna signal-to-quantization noise ratio for conventional linear beamformers to provide the desired range of the number of analog-to-digital converter (ADC) bits, providing system insights for reliable FD operation in regard to the ADC resolution and beamforming strategy. Motivated by the insights, we then propose an SI-aware beamforming method that mitigates residual SI and quantization distortion. The resulting spectral efficiency (SE) maximization problem is decomposed into two tractable subproblems solved via alternating optimization: precoder and combiner design. The precoder optimization is formulated as a generalized eigenvalue problem, where the dominant eigenvector yields the best stationary solution through power iteration, while the combiner is derived as a quantization-aware minimum meansquared error (MMSE) filter. Numerical studies show that the number of required ADC bits with the proposed beamforming falls within the derived theoretical range while achieving the highest SE compared to benchmarks.

Low Earth orbit (LEO) satellite networks with mega constellations have the potential to provide 5G and beyond services ubiquitously. However, these networks may introduce mutual interference to both satellite and terrestrial networks, particularly when sharing spectrum resources. In this paper, we present a system-level performance analysis to address these interference issues using the tool of stochastic geometry. We model the spatial distributions of satellites, satellite users, terrestrial base stations (BSs), and terrestrial users using independent Poisson point processes on the surfaces of concentric spheres. Under these spatial models, we derive analytical expressions for the ergodic spectral efficiency of uplink (UL) and downlink (DL) satellite networks when they share spectrum with both UL and DL terrestrial networks. These derived ergodic expressions capture comprehensive network parameters, including the densities of satellite and terrestrial networks, the path-loss exponent, and fading. From our analysis, we determine the conditions under which spectrum sharing with UL terrestrial networks is advantageous for both UL and DL satellite networks. Our key finding is that the optimal spectrum sharing configuration among the four possible configurations depends on the density ratio between terrestrial BSs and users, providing a design guideline for spectrum management. Simulation results confirm the accuracy of our derived expressions.

Inter-beam interference is a core challenge in low Earth orbit (LEO) satellite communications, driven by dense constellations, aggressive frequency reuse, and overlapping beam footprints. To address this, we propose space–time beamforming, a novel approach that jointly exploits spatial and temporal channel characteristics—specifically the angle of arrival (AoA) and relative Doppler shift—to optimize transmission between moving satellites and ground users. By synthesizing a virtual array-of-subarrays across repeated transmissions, this method effectively expands the aperture and forms ultra-narrow beams, sharply suppressing interference leakage to neighboring users. We develop two strategies within this framework: space-time zero-forcing (ST-ZF) and space-time signal-to-leakage-plus-noise ratio (ST-SLNR) beamforming. In partially connected networks, ST-ZF provides a 3 dB SNR gain over conventional maximum ratio transmission (MRT). In more general interference scenarios, ST-SLNR delivers significant improvements in sum spectral efficiency. While temporal repetition introduces a rate trade-off, it also enables finer spatial discrimination through Doppler-induced temporal signatures. Our analysis and simulations demonstrate that space-time beamforming offers a powerful and adaptable solution for interference mitigation in next-generation LEO satellite systems, unlocking better spectral efficiency and more reliable connectivity in densely served orbital environments.

Semantic communications based on deep joint source-channel coding (JSCC) aim to improve communication efficiency by transmitting only task-relevant information. How-ever, ensuring robustness to the stochasticity of communication channels remains a key challenge in learning-based JSCC. In this paper, we propose a novel regularization technique for learning-based JSCC to enhance robustness against channel noise. The proposed method utilizes the Kullback-Leibler (KL) divergence as a regularizer term in the training loss, measuring the discrepancy between two posterior distributions: one under noisy channel conditions (noisy posterior) and one for a noise-free system (noise-free posterior). We further show that the expectation of the KL divergence given the encoded representation can be analytically approximated using the Fisher information matrix and the covariance matrix of the channel noise. Notably, the proposed regularization is architecture-agnostic, making it broadly applicable to general semantic communication systems over noisy channels.

We propose a novel approach for channel state information (CSI) compression in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, where the frequency-domain channel matrix is treated as a high-dimensional complex-valued image. Our method leverages transformer-based nonlinear transform coding (NTC), an advanced deep-learning-driven image compression technique that generates a highly compact binary representation of the CSI. Unlike conventional autoencoder-based CSI compression, NTC optimizes a nonlinear mapping to produce a latent vector while simultaneously estimating its probability distribution for efficient entropy coding. By exploiting the statistical independence of latent vector entries, we integrate a transformer-based deep neural network with a scalar nested-lattice uniform quantization scheme, enabling low-complexity, multi-rate CSI feedback that dynamically adapts to varying feedback channel conditions. The proposed multi-rate CSI compression scheme achieves state-of-the-art rate-distortion performance, outperforming existing techniques with the same number of neural network parameters. Simulation results further demonstrate that our approach provides a superior rate-distortion trade-off, requiring only 6 % of the neural network parameters compared to existing methods, making it highly efficient for practical deployment.

Deep polar codes are a class of pre-transformed polar codes that employ a multi-layered polar kernel transformation strategy to enhance code performance in short blocklength regimes. However, like conventional polar codes, their block length is constrained to powers of two, as the final transformation layer uses a conventional polar kernel matrix. In this paper, we propose a novel rate-matching technique for deep polar codes using a code extension methodology, particularly effective when the desired code length slightly exceeds a power of two. The key idea is to exploit the layered structure of deep polar codes by concatenating polar codewords generated at each layer. Based on this structure, we also develop an efficient decoding algorithm for the proposed rate-matching deep polar codes by leveraging soft-output successive cancellation list decoding. Furthermore, we provide a decoding error probability analysis that serves as a foundation for the proposed rate-matching code design algorithms. Extensive simulations confirm that the proposed rate-matching deep polar codes provide notable coding gains, particularly at higher code rates.

Block Orthogonal Sparse Superposition (BOSS) codes are a promising class of joint coded modulation techniques that can closely approach the finite-blocklength capacity with low-complexity decoding at low code rates under Gaussian channels. However, in fading channels, the performance of BOSS codes degrades considerably due to varying channel fading effects on coded symbols. This paper presents a unified approach to extending BOSS codes to practical fading scenarios and introduces novel joint demodulation and decoding solutions. For fast-fading channels, we propose a minimum mean square error approximation maximum a posteriori (MMSE-A-MAP) algorithm that integrates demodulation and decoding when channel state information is available at the receiver (CSIR). Additionally, for block-fading channels without CSIR, we introduce a joint demodulation and decoding method, referred to as the non-coherent sphere decoding (NSD) algorithm. Simulation results demonstrate that BOSS codes with MMSE-A-MAP decoding outperform 5G polar codes, while the NSD algorithm achieves performance comparable to quasi-maximum likelihood decoding but with significantly reduced complexity. Both decoding methods can be implemented for parallel processing, allowing them to meet low-latency requirements. Furthermore, real-time simulations on a software-defined radio testbed validate the feasibility of using BOSS codes for low-power transmission.

Deep polar codes, which employ multi-layered polar kernel pre-transforms in series, are recently introduced variants of pre-transformed polar codes. These codes can reduce the number of minimum-weight codewords, thus closely achieving finite-block length capacity with successive cancellation list (SCL) decoders in certain scenarios. However, for low-latency applications requiring small SCL decoder list sizes, reducing minimum-weight codewords doesn’t necessarily improve decoding performance. To address this limitation, we propose an alternative pre-transform technique to enhance the suitability of polar codes for SCL decoders with practical list sizes. Leveraging the fact that the SCL decoding error event set can be decomposed into two exclusive error event sets, our approach applies two different types of pre-transformations, each targeting the reduction of one of the two error event sets. Extensive simulation results across various block lengths and code rates demonstrate that our codes consistently outperform existing state-of-the-art pre-transformed polar codes, including CRC-aided polar codes and polarization-adjusted convolutional codes, when decoded using SCL decoders with small list sizes.

In frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) for precoding is vastly challenging due to the tremendous feedback overhead with the growing number of antennas. Utilizing uplink pilots for downlink CSI estimation is a promising approach that can eliminate CSI feedback. However, the downlink CSI estimation accuracy diminishes significantly as the number of channel paths increases, resulting in reduced spectral efficiency. In this paper, we demonstrate that achieving downlink spectral efficiency comparable to perfect CSI is feasible by combining uplink CSI with limited downlink CSI feedback information. Our proposed downlink CSI feedback strategy transmits quantized phase information of downlink channel paths, deviating from conventional limited methods. We put forth a mean square error (MSE)-optimal downlink channel reconstruction method by jointly exploiting the uplink CSI and the limited downlink CSI. Armed with the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for phase quantization. Subsequently, we present an optimal feedback bit allocation method for minimizing the MSE in the reconstructed channel through phase quantization. Utilizing a robust downlink precoding technique, we establish that the proposed downlink channel reconstruction method is sufficient for attaining a sum-spectral efficiency comparable to perfect CSI.

Multibeam satellite communication systems are promising to achieve high throughput. To achieve high performance without substantial overheads associated with channel state information (CSI) of ground users, we consider a fixed-beam precoding approach, where a satellite forms multiple fixed-beams without relying on CSI, then selects a suitable user set for each beam. Upon this precoding method, we put forth a satellite equipped with massive multiple-input multiple-output (MIMO), by which inter-beam interference is efficiently mitigated by narrowing the corresponding beam width. By modeling the ground users’ locations via a Poisson point process, we rigorously analyze the achievable performance of the presented multibeam satellite system. In particular, we investigate the asymptotic scaling laws that reveal the interplay between the user density, the number of beams, and the number of antennas. Our analysis offers critical design insights for the multibeam satellite with massive MIMO: i) If the user density scales proportionally with the number of antennas, the considered precoding can achieve a linear fraction of the optimal rate in the asymptotic regime. ii) A certain additional scaling factor for the user density is needed as the number of beams increases to maintain the asymptotic optimality.

Nonlinear self-interference cancellation (SIC) techniques are essential for enabling full-duplex communication systems, which can offer spectral efficiencies twice that of traditional half-duplex systems. The challenge of nonlinear SIC is similar to the classic problem of system identification in adaptive filter theory, whose crux lies in constructing the optimal nonlinear basis functions of a nonlinear system. This becomes especially difficult when the system input has a non-stationary distribution, as is the case in practical wireless systems. In this paper, we propose a novel algorithm for nonlinear digital SIC that adaptively constructs orthonormal polynomial basis functions according to the non-stationary moments of the transmit signal. By combining these basis functions with the least mean squares (LMS) algorithm, we introduce a new SIC technique, called the adaptive orthonormal polynomial LMS (AOP-LMS) algorithm. To reduce computational complexity for practical systems, we augment our approach with a precomputed look-up table, which maps a given modulation and coding scheme to its corresponding basis functions. Numerical simulation indicates that our proposed method surpasses existing state-of-the-art SIC algorithms in terms of convergence speed and mean squared error when the transmit signal is non-stationary, such as with adaptive modulation and coding. Experimental evaluation with a wireless testbed further confirms that our proposed approach outperforms existing digital SIC algorithms in practical systems.

Digital self-interference cancellation (SIC) plays an essential role in realizing both in-band and sub-band full-duplex wireless systems. In this work, we present a novel digital SIC algorithm which adaptively cancels the nonlinear self-interference (SI) introduced by nonideal hardware in real-world wireless systems. The proposed approach extends our recent work, called AOP-LMS, to cancel the aggregate SI incurred at each receive antenna in a multi-antenna transceiver. We introduce a means to trade off SIC performance for complexity by simplifying our approach to scale with the number of beams (spatial streams) rather than the number of transmit antennas. We then show that the proposed approach is capable of cancelling both in-band SI and sub-band SI via a digital frequency compensation. Simulation confirms that our proposed approach reliably cancels in-band SI, sub-band SI, and both concurrently—all to near the noise floor.

In frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) becomes challenging due to the tremendous feedback overhead that increases with the number of antennas. Using uplink pilots to estimate downlink CSI is a promising approach that can eliminate the need for CSI feedback, but its accuracy decreases significantly as the number of channel paths increases. In this paper, we propose a mean square error (MSE)-optimal downlink channel reconstruction method that jointly utilizes uplink CSI and limited downlink CSI. With the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for channel phase quantization and show the optimal feedback bit allocation method to minimize the MSE. Harnessing robust downlink precoding, we demonstrate that the proposed downlink channel reconstruction is sufficient to achieve a sum-spectral efficiency comparable to that with perfect downlink CSI.

Integrated satellite-terrestrial networks (ISTNs) has recently been significant interest to attain synergistic gains in expanded coverage and transmission rate enhancement. Despite the potential of ISTNs, a comprehensive mathematical performance analysis framwork is lacking, so this paper introduces a tractable approach to analyze the downlink coverage performance of ISTNs, where each network operates with orthogonal frequency bands. We model the spatial distribution of terrestrial and satellite base stations (BSs) using homogeneous Poisson point processes arranged on concentric spheres with varying radii. Central to our analysis is a displacement principle that transforms BS locations on different spheres into annuli while preserving the distance distribution to the typical user. By incorporating the effects of Shadowed-Rician fading on satellite channels, we derive analytical expression for coverage in the ISTN while keeping full generality. Our key finding is that network performance depends on the density ratio of users associated with the network according to the density and the channel parameters of each network. Through simulations, we validate the precision of our derived expression.

This paper presents an algorithm for finding the optimal configuration of active reconfigurable intelligent surface (RIS) when both transmitter and receiver are equipped with a single antenna each. The resultant configuration is globally optimal and it takes linear time for the computation. Moreover, there is a closed-form expression for the optimal configuration when the direct link vanishes, which enables further analysis.

For frequency-division-duplexing (FDD) systems, channel state information (CSI) should be fed back from the user terminal to the base station. This feedback overhead becomes problematic as the number of antennas grows. To alleviate this issue, we propose a flexible CSI compression method using variational autoencoder (VAE) with an entropy bottleneck structure, which can support multi-rate and variable-length operation. Numerical study confirms that the proposed method outperforms the existing CSI compression techniques in terms of normalized mean squared error.

Distributed learning is an effective approach to accelerate model training by using parallel computing power of multiple workers. However, substantial communication delays arise between workers and a parameter server due to the massive costs associated with communicating gradients. SignSGD with majority voting (signSGD-MV) is a simple yet effective optimizer that reduces communication costs through sign quantization, but its convergence rate significantly decreases when adversarial workers arbitrarily manipulate datasets or local gradient updates. In this paper, we consider a distributed learning problem where the workforce comprises a mixture of honest and adversarial workers. In this setting, we show that the convergence rate can remain invariant as long as the number of honest workers providing trustworthy local updates to the parameter server exceeds the number of adversarial workers. The key idea behind this counter-intuitive result is our novel aggregation method, signSGD with federated defense (signSGD-FD). Unlike traditional approaches, signSGD-FD utilizes the gradient information sent by adversarial workers with appropriate weights, obtained through gradient sign decoding. Experimental results demonstrate that signSGD-FD achieves superior convergence rates compared to traditional algorithms in various adversarial attack scenarios.

Polar codes are the first explicitly constructed error-correcting codes over binary input memoryless channels that achieve provably asymptotic capacity using low-complexity encoding and decoding. Applying upper-triangular pre-transform before polar encoding improves the distance property of the existing polar codes. Although it is known that well-designed pre-transformed polar codes can achieve the normal approximation bound in finite-blocklength regime, achieving that limit requires huge decoding latency, which is undesirable in practical communication system. In this paper, we adopt sparsely applied pre-transform to enhance the weight spectrum of codewords and be decodable under successive cancellation list decoder with small list size. We also propose an algorithm for selecting indices to which pre-transforms are applied. Simulation results confirm that proposed codes surpass 5G standard polar codes with cyclic redundancy check precoding by supporting the effectiveness of sparse pre-transform.

This paper presents signSGD with federated voting (signSGD-FV), a communication-efficient distributed learning algorithm with heterogeneous edge workers. The FV aggregation leverages the log-likelihood ratio (LLR) weight assigned to each worker, and performs weighted majority voting aggregation by interpreting the conventional signSGD with majority voting (signSGD-MV) algorithm in a coding-theoretical approach. The LLR weights are estimated based on the aggregation results determined by the sign votes of workers, which shows the essence of federated voting. Our theoretical analyses and the experimental results on real-world datasets demonstrate the superiority of signSGD-FV for both communication efficiency and learning performance when the workers employ different sizes of mini-batches.

In this paper, we introduce a novel class of pre-transformed polar codes, termed asdeep polar codes. We first present a deep polar encoder that harnesses a series of multi-layered polar transformations with varying sizes. Our approach to encoding enables a low-complexity implementation while significantly enhancing the weight distribution of the code. Moreover, our encoding method offers flexibility in rate-profiling, embracing a wide range of code rates and blocklengths. Next, we put forth a low-complexity decoding algorithm called successive cancellation list withbackpropagation parity checks(SCL-BPC). This decoding algorithm leverages the parity check equations in the reverse process of the multi-layered pre-transformed encoding for SCL decoding. Additionally, we present a low-latency decoding algorithm that employs parallel SCL decoding by treating partially pre-transformed bit patterns as additional frozen bits. Through simulations, we demonstrate that deep polar codes outperform existing pre-transformed polar codes in terms of block error rates across various code rates under short block lengths, while maintaining low encoding and decoding complexity. Furthermore, we show that concatenating deep polar codes with cyclic-redundancy-check codes can achieve the meta-converse bound of the finite block length capacity within 0.4 dB in some instances.

Integrated sensing and communication (ISAC) is widely recognized as a fundamental enabler for future wireless communications. In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems. To this end, we adopt either a radar transmit beam mean square error (MSE) or receive signal-to-clutter-plus-noise ratio (SCNR) as a radar performance constraint of a sum SE maximization problem. To resolve inherent challenges such as non-convexity and imperfect CSI, we reformulate the problems and identify first-order optimality conditions for the joint radar and communication beamformer. Turning the condition to a nonlinear eigenvalue problem with eigenvector dependency (NEPv), we develop an alternating method which finds the joint beamformer through power iteration and a Lagrangian multiplier through binary search. The proposed framework encompasses both the radar metrics and is robust to channel estimation error with low complexity. Simulations validate the proposed methods. In particular, we observe that the MSE and SCNR constraints exhibit complementary performance depending on the operating environment, which manifests the importance of the proposed comprehensive and robust optimization framework.

This paper studies the optimization of an active reconfigurable intelligent surface (RIS) under two power budget models. Two algorithms, one for each power constraint, for finding the optimal RIS configuration are proposed under the proviso that both the transmitter and receiver are equipped with a single antenna each. The computational complexities of these methods are linear in the number of RIS elements, and the resultant configurations are globally optimal.

With increasing frequencies, bandwidths, and array apertures, the phenomenon of beam squint arises as a serious impairment to beamforming. Fully digital arrays with true time delay per antenna element are a potential solution, but they require downconversion at each element. This paper shows that hybrid arrays can perform essentially as well as digital arrays once the number of radio-frequency chains exceeds a certain threshold that is far below the number of elements. This threshold is determined by only a few physical parameters—bandwidth, array size, and beamforming direction—and can be expressed in a remarkably simple closed form. The result is robust, holding also for suboptimum yet highly appealing beamspace architectures.

Transmitter channel state information (CSIT) is indispensable for the spectral efficiency gains offered by massive multiple-input multiple-output (MIMO) systems. In a frequency-division-duplexing (FDD) massive MIMO system, CSIT is typically acquired through downlink channel estimation and user feedback, but as the number of antennas increases, the over-head for CSI training and feedback per user grows, leading to a decrease in spectral efficiency. In this paper, we show that, using uplink pilots in FDD, the downlink sum spectral efficiency gain with perfect downlink CSIT is achievable when the number of antennas at a base station is infinite by leveraging the partial channel reciprocity between uplink and downlink channels. Specifically, the key idea showing our result is the mean squared error-optimal downlink channel reconstruction method using uplink pilots, and the robust downlink precoding method harnessing the reconstructed channel with the error covariance matrix. Our simulation results show that our proposed precoding method can attain comparable sum spectral efficiency to zero-forcing precoding with perfect downlink CSIT, without CSI training and feedback.

This paper addresses cooperative link scheduling problems for base station (BS) aided device-to-device (D2D) communications using limited channel state information (CSI) at BS. We first derive the analytical form of ergodic sum-spectral efficiency as a function of network parameters, assuming statistical CSI at the BS. However, the optimal link scheduling, which maximizes the ergodic sum-spectral efficiency, becomes computationally infeasible when network density increases. To overcome this challenge, we present a low-complexity link scheduling algorithm that divides the D2D network into sub-networks and identifies the optimal link scheduling strategy per sub-network. Furthermore, we consider the scenario when the statistical CSI is not available to the BS. In such cases, we propose a quasi-optimal scalable link scheduling algorithm that utilizes one-bit feedback information from D2D receivers. The algorithm clusters the links and applies the UCB algorithm per cluster using the collected one-bit feedback information. We highlight that even with reduced scheduling complexity, the proposed algorithm identifies a link scheduling action that ensures optimality within a constant throughput gap. We also demonstrate through simulations that the proposed algorithm achieves higher sum-spectral efficiency than the existing link scheduling algorithms, even without explicit CSI or network parameters knowledge.

In this paper, we investigate the coverage performance of downlink satellite networks employing dynamic coordinated beamforming. Our approach involves modeling the spatial arrangement of satellites and users using Poisson point processes situated on concentric spheres. We derive an analytical expression for the coverage probability which is formulated in terms of various parameters, including the number of antennas per satellite, satellite density, fading characteristics, and path-loss exponent. This coverage probability validates the advantages of coordinated beamforming from a spatial average perspective. Our primary finding is that dynamic coordinated beamforming significantly improves coverage compared to the absence of satellite coordination, in proportion to the number of antennas on each satellite. Moreover, we observe that the optimal cluster size, which maximizes the ergodic spectral efficiency, increases with higher satellite density, provided that the number of antennas on the satellites is sufficiently large. To offer a more intuitive understanding, we also develop an approximation for the coverage probability. Furthermore, by considering the in-cluster geometry of the coordinated satellite set, we derive an approximate coverage probability conditioned on in-cluster geometry. Our findings are corroborated by simulation results, confirming the accuracy of the derived expressions.

The joint design of secure precoding and artificial noise (AN) transmission scheme is promising to improve secrecy performance. However, in downlink multi-user multiple-input multiple-output (MU-MIMO) systems with multiple eavesdroppers, joint design of secure precoding and AN structure involves several challenges: an objective function is non-convex and non-smooth, and a precoding matrix and AN matrix have different design principles. Classically, to jointly design precoding and AN covariance matrix, an alternating optimization approach is used which has limitations in terms of the secrecy rate performance since it does not offer joint and simultaneous optimization of the precoding and AN covariance matrices. In this paper, we propose a novel optimization framework that optimizes the precoder and the AN covariance matrix jointly and simultaneously to maximize the secrecy rate. First, we approximate the objective function to a tractable non-convex form. Next, we derive the first-order optimality condition by leveraging the nonlinear eigenvalue problem (NEP) form. Finally, we utilize an efficient technique with low computational complexity for identifying the major eigenvector of the NEP which corresponds to the best stationary point. Simulations illustrate that the proposed methods enhance the secrecy rate performance compared to the existing secure precoding methods.

Satellite communications have been promising to guarantee global coverage with high capacity. In this paper, we analyze coverage performance of satellite networks with a distance-dependent line-of-sight (LOS) and non-LOS (NLOS) channel propagation probability to incorporate shadowing effect. Extending the stochastic geometry-based network analysis for terrestrial networks, we model the satellite network and users as a Poisson point process and derive an theoretical coverage probability expression to provide analytical understanding of the satellite network. Simulation results verify the exactness of the derived expression. The derived expression includes network parameters for satellite density and altitude, channel fading, pathloss, and the LOS probability, and provides insights on satel-lite networks. Our key finding is that NLOS channel propagation benefits the coverage performance by reducing the interference from non-associated satellites, and the higher NLOS probability is desirable to improve the coverage performance as the network becomes denser.

Nonlinear self-interference cancellation (SIC) is a crucial technology that has the potential to double the spectral efficiency in full-duplex communications when compared to a half-duplex system. The nonlinear SIC challenge is similar to the classic problem of system identification in adaptive filter theory. The crux of this problem lies in identifying the optimal nonlinear basis function that represents the nonlinear system, which becomes especially difficult when the input data has non-stationary distributions. In this paper, we propose a novel algorithm for nonlinear digital SIC that uses adaptive orthogonal polynomial basis functions. Our algorithm adaptively builds a set of orthogonal polynomial basis functions by utilizing the moments of the transmit data symbols. By combining these basis functions with the least mean squares (LMS) algorithm, we introduce a new SIC algorithm named the adaptive orthonormal polynomial LMS (AOP-LMS) algorithm. Simulation results indicate that our proposed SIC method surpasses existing state-of-the-art SIC algorithms in terms of convergence speed and mean squared error loss, especially in non-stationary transmit data symbol environments.

The need for channel state information (CSIT) is crucial for the improved spectral efficiency of massive multiple-input multiple-output (MIMO) systems. In FDD massive MIMO systems, CSIT is obtained through downlink channel estimation and user feedback, but this process becomes challenging as the number of antennas increases, resulting in reduced spectral efficiency. In this paper, we show that even in FDD systems, the TDD massive MIMO gain is attainable without explicit CSIT training and feedback, by using UL pilots. We present a novel DL channel reconstruction method from uplink pilots and a robust downlink precoding technique, proving that the FDD massive MIMO gains are achievable without CSI training and feedback. Our results are verified through system-level simulations.

This paper presents S3GD-MV, a communication-efficient distributed learning algorithm that combines the benefits of sparsification and sign quantization. In S3GD-MV, each worker selects the top-K largest components of the local gradient vector in magnitude and only sends the signs of the selected components to the server, which aggregates the signs via a majority vote and returns the result. Our analysis shows that when the sparsification parameter is properly selected, S3GD-MV converges as quickly as signSGD for smooth non-convex functions, but with significantly reduced communication costs. Simulation results of training a convolutional neural network on the MNIST dataset show that S3GD-MV reduces communication costs compared to other conventional optimizers, while improving test accuracy.

The number of devices served by baseband stations is constantly increasing due to the rising data traffic in modern communication systems. In order to support large-scale multi-user multiple-input multiple-output (MU-MIMO) systems and achieve their capacity, it is necessary to consider power allocation and user selection along with precoding. This paper introduces a scalable MU-MIMO precoding processor that solves the joint optimization problem for precoding, power allocation, and user selection. We define custom vector instructions and dedicate vector arithmetic operators based on the RV32IM instruction set architecture to efficiently support various MU-MIMO baseband processing scenarios. The proposed vector operators include parallel dual-precision multipliers to enable energy-efficient processing by adjusting the computing resolution of each step without degrading the algorithm-level quality. The proposed processor is fabricated using 28nm CMOS technology and is capable of solving the state-of-the-art joint optimization problem in only 0.51ms for the$64\times 64$large-scale MU-MIMO configuration. Our processor achieves up to 17.4 times higher processing efficiency compared to previous precoder design, even when supporting the largest number of users and the most complicated algorithm.

Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.

Low-rate and short-packet transmissions are important for ultra-reliable low-latency communications (URLLC). In this paper, we put forth a new family of sparse superposition codes for URLLC, called block orthogonal sparse superposition (BOSS) codes. We first present a code construction method for the efficient encoding of BOSS codes. The key idea is to construct codewords by the superposition of the orthogonal columns of a dictionary matrix with a sequential bit mapping strategy. We also propose an approximate maximum a posteriori probability (MAP) decoder with two stages. The approximate MAP decoder reduces the decoding latency significantly via a parallel decoding structure while maintaining a comparable decoding complexity to the successive cancellation list (SCL) decoder of polar codes. Furthermore, to gauge the code performance in the finite-blocklength regime, we derive an exact analytical expression for block-error rates (BLERs) of single-layered BOSS codes in terms of relevant code parameters. Lastly, we present a cyclic redundancy check aided-BOSS (CA-BOSS) code with simple list decoding to boost the code performance. Our experiments verify that CA-BOSS codes with the simple list decoder outperform CA-polar codes with SCL decoding in the low-rate and finite-blocklength regimes while achieving the finite-blocklength capacity upper bound within one dB of signal-to-noise ratio.

This paper deals with line-of-sight (LOS) MIMO communication via an intelligent reflecting surface (IRS). It is shown that the number of spatial degrees of freedom (DOF) afforded by this setting grows in proportion with the IRS aperture, as opposed to being dictated by the transmit and receive apertures; this buttresses the interest in IRS deployments at mmWave and terahertz frequencies, with wavelengths and transmission ranges small enough to enable LOS MIMO. Explicit and simple-to-implement IRS phase shifts are put forth that achieve, not only the maximum number of DOF, but the capacity, under a certain geometrical condition. The insights leading to the proposed phase shifts, and to the optimality condition itself, are asymptotic in nature, yet extensive simulations confirm that the performance is excellent for a broad range of settings and even if the optimality condition is not strictly met.

Motivated by the widespread adoption of the parabolic wavefront model for line-of-sight (LOS) multiple-input multiple-output (MIMO) communication, this paper presents a comprehensive analysis of this model’s validity and simple conditions that ensure its applicability. Then, with the model’s scope clearly delineated, the paper expounds a number of properties of the channel that results from applying it. Connections are drawn among these properties under the umbrella of a Fourier interpretation, and their significance to LOS MIMO communication is substantiated.

In this paper, we present a communication-efficient federated learning framework inspired by quantized compressed sensing. The presented framework consists of gradient compression for wireless devices and gradient reconstruction for a parameter server (PS). Our strategy for gradient compression is to sequentially perform block sparsification, dimensional reduction, and quantization. By leveraging both dimension reduction and quantization, our strategy can achieve a higher compression ratio than one-bit gradient compression. For accurate aggregation of local gradients from the compressed signals, we put forth an approximate minimum mean square error (MMSE) approach for gradient reconstruction using the expectation-maximization generalized-approximate-message-passing (EM-GAMP) algorithm. Assuming Bernoulli Gaussian-mixture prior, this algorithm iteratively updates the posterior mean and variance of local gradients from the compressed signals. We also present a low-complexity approach for the gradient reconstruction. In this approach, we use the Bussgang theorem to aggregate local gradients from the compressed signals, then compute an approximate MMSE estimate of the aggregated gradient using the EM-GAMP algorithm. We also provide a convergence rate analysis of the presented framework. Using the MNIST dataset, we demonstrate that the presented framework achieves almost identical performance with the case that performs no compression, while significantly reducing communication overhead for federated learning.

Satellite networks are promising to provide ubiquitous and high-capacity global wireless connectivity. Traditionally, satellite networks are modeled by placing satellites on a grid of multiple circular orbit geometries. Such a network model, however, requires intricate system-level simulations to evaluate coverage performance, and analytical understanding of the satellite network is limited. Continuing the success of stochastic geometry in a tractable analysis for terrestrial networks, in this paper, we develop novel models that are tractable for the coverage analysis of satellite networks using stochastic geometry. By modeling the locations of satellites and users using Poisson point processes on the surfaces of concentric spheres, we characterize analytical expressions for the coverage probability of a typical downlink user as a function of relevant parameters, including path-loss exponent, satellite height, density, and Nakagami fading parameter. Then, we also derive a tight lower bound of the coverage probability in tractable expression while keeping full generality. Leveraging the derived expression, we identify the optimal density of satellites in terms of the height and the path-loss exponent. Our key finding is that the optimal average number of satellites decreases logarithmically with the satellite height to maximize the coverage performance. Simulation results verify the exactness of the derived expressions.

Rate-splitting multiple access (RSMA) is a general multiple access scheme for downlink multi-antenna systems embracing both classical spatial division multiple access and more recent non-orthogonal multiple access. Finding a linear precoding strategy that maximizes the sum spectral efficiency of RSMA is a challenging yet significant problem. In this paper, we put forth a novel precoder design framework that jointly finds the linear precoders for the common and private messages for RSMA. Our approach is first to approximate the non-smooth minimum function part in the sum spectral efficiency of RSMA using a LogSumExp technique. Then, we reformulate the sum spectral efficiency maximization problem as a form of the log-sum of Rayleigh quotients to convert it into a tractable form. By interpreting the first-order optimality condition of the reformulated problem as an eigenvector-dependent nonlinear eigenvalue problem, we reveal that the leading eigenvector of the derived optimality condition is a local optimal solution. To find the leading eigenvector, we propose an algorithm inspired by a power iteration. Simulation results show that the proposed RSMA transmission strategy provides significant improvement in the sum spectral efficiency compared to the state-of-the-art RSMA transmission methods.

Block orthogonal sparse superposition (BOSS) codes are a class of sparse superposition codes with orthogonality among sub-codewords. This orthogonal structure allows using a near-optimal maximum a posteriori (MAP) decoder with low complexity. Multi-path fading environments, however, destroy the intrinsic orthogonal property of BOSS codewords and result in a catastrophic loss of decoding performance. In this paper, we suggest a novel decoding algorithm of BOSS codes for multi-path fading channels. The proposed decoder comprises two-stage operations: i) minimum mean square error (MMSE) equalization and ii) approximate element-wise MAP decoding. The MMSE equalization restores the orthogonality in the codewords. Then, the element-wise MAP decoding identifies the non-zero positions of sparse message vectors. It turns out that BOSS codes with the proposed decoder provide a considerable performance improvement in terms of block error rate compared to polar codes in the short blocklength regime, while requiring polynomial time decoding complexity. To verify the performance gains, we provide simulation results. We also study the possibility of covert communications using BOSS codes.

In this paper, we develop novel models that are tractable for the coverage and rates analysis using stochastic geometry. By modeling the locations of satellites and users using Poisson point processes (PPPs) on the surfaces of concentric spheres with distinct radii, we characterize analytical expressions for the coverage probability of a typical downlink user as a function of relevant parameters. Then, we also derive a tight lower bound of the coverage probability in closed-form expression while keeping full generality. Leveraging the derived expression, we identify the optimal density of satellites in terms of the altitude. Our key finding is that the required average number of satellites scales down logarithmically with the network height to maximize the coverage performance. Simulation results verify the exactness of the derived expressions.

In this paper, we propose a precoding method to maximize energy efficiency (EE) in a downlink multiuser massive multiple-input multiple-output system with low-resolution quantizers. To this end, we formulate an EE maximization problem with respect to precoders by incorporating the quantization errors caused by the low-resolution quantizers. The main challenges exist: i) the quantization errors are entangled with the precoders, ii) a objective function is non-convex, and iii) unlike a spectral efficiency (SE) maximization problem, a precoding power needs to be jointly optimized. To address these challenges, we first adopt a Dinkenbach method and reformulate the EE problem to a more tractable form. We further decompose the problem into an optimal precoding direction and transmit power problems. To find the optimal direction, we derive a first-order Karush–Kuhn–Tucker (KKT) condition and interpret the condition as a generalized eigenvalue problem. Accordingly, adopting a generalized power iteration-based precoding method, we find the principal eigenvector which is the best sub-optimal precoder. Regarding the transmit power optimization, the objective function becomes concave for given other variables. Hence, the transmit power level is optimized by using a gradient descent method. Via simulations, we demonstrate that the proposed algorithm provides the highest EE performance compared to baseline methods.

The compressive sensing (CS) based sparse vector coding (SVC) method is one of the promising ways for the next-generation ultra-reliable and low-latency communications. In this paper, we present advanced algorithm-hardware co-optimization schemes for realizing a cost-effective SVC decoding architecture. The previous maximum a posteriori subspace pursuit (MAP-SP) algorithm is newly modified to relax the computational overheads by applying novel residual forwarding and LLR approximation schemes. A fully-pipelined parallel hardware is also developed to support the modified decoding algorithm, reducing the overall processing latency, especially at the support identification step. In addition, an advanced least-square-problem solver is presented by utilizing the parallel Cholesky decomposer design, further reducing the decoding latency with parallel updates of support values. The implementation results from a 22nm FinFET technology showed that the fully-optimized design is 9.6 times faster while improving the area efficiency by 12 times compared to the baseline realization.

Communication efficiency is of importance for wireless federated learning systems. In this paper, we propose a communication-efficient strategy for federated learning over multiple-input multiple-output (MIMO) multiple access channels (MACs). The proposed strategy comprises two components. When sending a locally computed gradient, each device compresses a high dimensional local gradient to multiple lower-dimensional gradient vectors using block sparsification. When receiving a superposition of the compressed local gradients via a MIMO-MAC, a parameter server (PS) performs a joint MIMO detection and the sparse local-gradient recovery. Inspired by the turbo decoding principle, our joint detection-and-recovery algorithm accurately recovers the high-dimensional local gradients by iteratively exchanging their beliefs for MIMO detection and sparse local gradient recovery outputs. We then analyze the reconstruction error of the proposed algorithm and its impact on the convergence rate of federated learning. From simulations, our gradient compression and joint detection-and-recovery methods diminish the communication cost significantly while achieving identical classification accuracy for the case without any compression.

The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise for massive multiple-input multiple-output (MIMO) systems. This paper considers a precoding optimization problem for maximizing EE in quantized downlink massive MIMO systems. To this end, we jointly optimize an active antenna set, precoding vectors, and allocated power; yet acquiring such joint optimal solution is challenging. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. To optimize precoding power for given precoding direction, we adopt a gradient descent algorithm for the EE maximization. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set, the precoding direction, and allocated power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems.

A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource. By extremely high signaling overheads for both channel state information (CSI) acquisition and data sharing at a baseband unit (BBU), finding a joint transmission strategy with a significantly reduced signaling overhead is indispensable to achieve the cooperation gain in practical C-RANs. In this paper, we present a novel sparse joint transmission (sparse-JT) method for C-RANs, where the number of transmit antennas per unit area is much larger than the active downlink user density. Considering the effects of noisy-and-incomplete CSI and the quantization errors in data sharing by a finite-rate fronthaul capacity, the key innovation of sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming vectors, and power allocation to maximize a lower bound of the sum-spectral efficiency under the sparsity constraint of active RRHs. To find such a solution, we present a computationally efficient algorithm that guarantees to find a local-optimal solution for a relaxed sum-spectral efficiency maximization problem. By system-level simulations, we exhibit that sparse-JT provides significant gains in ergodic spectral efficiencies compared to existing joint transmissions.

This paper considers line-of-sight multiple-input multiple-output communication, of interest at millimeter-wave and sub-terahertz frequencies. Building on how, with an angular rotation dependent on the signal-to-noise ratio (SNR), uniform linear arrays (ULAs) can tightly approach the capacity of such channels, we assess the performance of rotatable uniform rectangular arrays (URAs). The changeover from ULAs to URAs is motivated by the interest in reducing the array footprints. For both isotropic and directive antennas, various types of rotatable URAs are devised that-except at very low SNRs-perform remarkably close to their ULA counterparts with considerably smaller footprints.

This paper introduces block orthogonal sparse su-perposition (BOSS) codes for efficient short-packet commu-nications over Gaussian channels. Unlike conventional sparse superposition codes, an encoder of BOSS code uses multiple unitary matrices as a fat dictionary matrix and maps information bits such that multiple subgroups of codewords are orthogonal. Exploiting this orthogonal property per group, a two-stage maximum a posteriori (MAP) decoding algorithm is presented. The key idea of the two-stage MAP decoder is to successively estimate the non-zero alphabets corresponding to the orthogonal columns in a dictionary matrix and the index of a sub-dictionary matrix containing the columns. This decoding algorithm achieves a near-optimal decoding performance while requiring polynomial time complexity in blocklength. Via simulations, we show that the proposed encoding and decoding techniques achieve enhanced block-error-rate performances in the short blocklength regime compared to the state-of-the-art coded modulation methods.

In this paper, we consider the problem of wireless federated learning based on sign stochastic gradient descent (signSGD) algorithm via a multiple access channel. When sending locally computed gradient's sign information, each mobile device requires to apply precoding to circumvent wireless fading effects. In practice, however, acquiring perfect knowledge of channel state information (CSI) at all mobile devices is infeasible. In this paper, we present a simple yet effective precoding method with limited channel knowledge, called sign-alignment precoding. The idea of sign-alignment precoding is to protect sign-flipping errors from wireless fadings. Under the Gaussian prior assumption on the local gradients, we also derive the mean squared error (MSE)-optimal aggregation function called Bayesian over-the-air computation (BayAirComp). Our key finding is that one-bit precoding with BayAirComp aggregation can provide a better learning performance than the existing precoding method even using perfect CSI with AirComp aggregation.

Cell densification is a key driver to increase area spectral efficiencies in multi-antenna cellular systems. As increasing the densities of base stations (BSs) and users that share the same spectrum, however, both inter-user-interference (IUI) and inter-cell interference (ICI) problems give rise to a significant loss in spectral efficiencies in such systems. To resolve this problem under the constraint of local channel state information per BS, in this paper, we present a novel noncooperative multi-user multiple-input multiple-output (MIMO) precoding technique, called signal-to-interference-plus-leakage-plus-noise-ratio (SILNR) maximization precoding. The key innovation of our distributed precoding method is to maximize the product of SILNRs of users per cell using local channel state information at the transmitter (CSIT). We show that our precoding technique only using local CSIT can asymptotically achieve the multi-cell cooperative bound attained by cooperative precoding using global CSIT in some cases. We also present a precoding algorithm that is robust to CSIT errors in multi-cell scenarios. By multi-cell system-level simulations, we demonstrate that our distributed precoding technique outperforms all existing noncooperative precoding methods considerably and can also achieve the multi-cell bound very tightly even with not-so-many antennas at BSs.

Short packet transmission is a key ingredient for the Internet-of-Things (IoT) communications that require extremely low latency. The pilot-based short packet transmission makes lower spectral efficiency and larger transmission delays. To resolve this problem, in this article, we present a generalized differential subcarrier index modulation (GDIM) for pilot-free short packet transmission. The central idea of GDIM is to map information bits to possible indices that can be constructed by joint row and column permutations of a unitary matrix. We show that the proposed GDIM achieves higher spectral efficiency than that attained by the existing differential modulation techniques, including differential subcarrier index modulation and differential Alamouti code. We present a noncoherent maximum-likelihood (ML) detection method and a low-complexity variant of it for GDIM. Furthermore, by analyzing the average bit-error-rate when applying GDIM, we propose an algorithm to optimize GDIM constellation sets. Simulation results demonstrate that the proposed GDIM outperforms the existing differential modulation techniques in terms of bit-error-rates for numerous target spectral efficiencies.

Cell densification is a key driver to increase area spectral efficiencies in multi-antenna cellular systems. As increasing the density of base stations (BSs) and users that share the same spectrum, however, both inter-user-interference (IUI) and inter-cell interference (ICI) problems give rise to a significant loss in spectral efficiencies in such systems. To resolve this problem under the constraint of local channel state information per BS, in this paper, we present a novel noncooperative multi-user multiple-input multiple-output (MIMO) precoding technique, called signal-to-interference-pulse-leakage-pulse-noise-ratio (SILNR) maximization precoding. The key innovation of our distributed precoding method is to maximize the product of SILNRs of users per cell using local channel state information at the transmitter (CSIT). We show that our precoding technique only using local CSIT can asymptotically achieve the multi-cell cooperative bound attained by cooperative precoding using global CSIT in some cases. We also present a precoding algorithm that is robust to CSIT errors in multi-cell scenarios. By multi-cell system-level simulations, we demonstrate that our distributed precoding technique outperforms all existing noncooperative precoding methods considerably and can also achieve the multi-cell bound very tightly even with not-so-many antennas at BSs.

This paper establishes an upper bound on the capacity of line-of-sight multiantenna channels over all possible antenna arrangements and shows that uniform linear arrays (ULAs) with an SNR-dependent rotation of transmitter and/or receiver can closely approach such capacity-and in fact achieve it at low and high SNR, and asymptotically in the numbers of antennas. Then, as an alternative to mechanically rotating ULAs, we propose to electronically select among multiple ULAs having a radial disposition at either transmitter or receiver, and we bound the shortfall from capacity as a function of the number of such ULAs. With only three ULAs, properly angled, 96% of the capacity can be achieved. Finally, we further introduce reduced-complexity precoders and linear receivers that capitalize on the structure of the channels spawned by these configurable ULA architectures.

We establish an upper bound on the information-theoretic capacity of line-of-sight (LOS) multiantenna channels with arbitrary antenna arrangements and identify array structures that, properly configured, can attain at least 96.6% of such capacity at every signal-to-noise ratio (SNR). In the process, we determine how to configure the arrays as a function of the SNR. At low- and high-SNR specifically, the configured arrays revert to simpler structures and become capacity-achieving.

In this paper, a new optimization framework is presented for the joint design of user selection, power allocation, and precoding in multi-cell multi-user multiple-input multiple-output (MU-MIMO) systems when imperfect channel state information at transmitter (CSIT) is available. By representing the joint optimization variables in a higher-dimensional space, the weighted sum-spectral efficiency maximization is formulated as the maximization of the product of Rayleigh quotients. Although this is still a non-convex problem, a computationally efficient algorithm, referred to as generalized power iteration precoding (GPIP), is proposed. The algorithm converges to a stationary point (local maximum) of the objective function and therefore it guarantees the first-order optimality of the solution. By adjusting the weights in the weighted sum-spectral efficiency, the GPIP yields a joint solution for user selection, power allocation, and downlink precoding. The GPIP can be extended to the multi-cell scenario where cooperative base stations perform joint user-cell selection and design their precodes by taking into account the inter-cell interference by sharing global imperfect CSIT. System-level simulations show the gains of the proposed approach with respect to conventional user selection and linear downlink precoding.

The use of one-bit analog-to-digital converters (ADCs) at a receiver is a power-efficient solution for future wireless systems operating with a large signal bandwidth and/or a massive number of receive radio frequency chains. This solution, however, induces high channel estimation error and therefore makes it difficult to perform the optimal data detection that requires perfect knowledge of likelihood functions at the receiver. In this paper, we propose a likelihood function learning method for multiple-input multiple-output (MIMO) systems with one-bit ADCs using a reinforcement learning approach. The key idea is to exploit input-output samples obtained from data detection, to compensate for the mismatch in the likelihood function. The underlying difficulty of this idea is a label uncertainty in the samples caused by a data detection error. To resolve this problem, we define a Markov decision process (MDP) to maximize the accuracy of the likelihood function learned from the samples. We then develop a reinforcement learning algorithm that efficiently finds the optimal policy by approximating the transition function and the optimal state of the MDP. Simulation results demonstrate that the proposed method provides significant performance gains for data detection methods that suffer from the mismatch in the likelihood function.

In this paper, a novel non-coherent communication technique called generalized differential index modulation (GDIM) is presented. The key idea of GDIM is to modulate information bits to a joint permutation of rows and columns for a unitary matrix. It is shown that the proposed GDIM achieves a higher spectral efficiency than that attained by the existing differential modulation techniques including differential subcarrier index modulation and differential Alamouti code. An average bit-error-rate for GDIM is analyzed and applied to optimize GDIM constellation sets. Simulation results demonstrate that the proposed GDIM outperforms the existing differential modulation techniques in terms of bit-error-rates.

The use of one-bit analog-to-digital converters (ADCs) at a receiver is a power-efficient solution for future wireless systems. This paper presents a likelihood function learning method that enables robust maximum-a-posteriori-probability (MAP) detection for time-varying multiple-input multiple-output systems with one-bit ADCs. The key idea is to track the temporal variations of likelihood functions by exploiting input-output samples obtained from data detection, each containing the likelihood function information at each time slot. To deal with the uncertainty of this information caused by a data detection error, a Markov decision process (MDP) is defined, which maximizes the accuracy of the likelihood function learned from the samples. Then a reinforcement learning algorithm is developed to solve this MDP in a computationally efficient manner. Simulation results demonstrate that the use of the proposed method significantly improves the robustness of MAP detection to both the channel estimation error and channel variations over time.

A novel channel estimation framework is presented for orthogonal frequency division multiplexing (OFDM) system that operates with low-precision analog-to-digital converters (ADCs). The framework is based on concatenated minimum mean square error (MMSE) estimation, which consists of an inner and an outer MMSE estimation blocks. The outer MMSE estimation finds the compound signal, i.e., the unknown channel multiplied by a pilot signal, from the nonlinear distortion of the received signal by the quantization and its mean square error (MSE). Using the estimated compound signal and its MSE, the inner MMSE estimation estimates the desired signal, i.e., the unknown channel value, assuming the resulting estimation error of the outer MMSE block follows the Gaussian distribution. One major finding is that the proposed framework is analytically tractable for quantifying the effective quantization error, unlike the widely-used Bussgang-based approach. From simulations, it is shown that the proposed channel estimation framework provides a significant gain over conventional Bussgang-based methods using the approximated covariance matrix of the quantization error.

A cloud radio access network (cloud-RAN) is a promising cellular architecture to increase both network spectral efficiency and energy efficiency. In the downlink transmission of cloud-RAN, a fundamental trade-off exists between the sum-spectral efficiency and the network power consumption induced by the fronthual links. To optimize this trade-off, it is essential to jointly identify a set of active remote radio heads (RRHs) and the beamforming vectors used at the active RRHs. To resolve this problem, this paper presents a novel group-sparse beamforming algorithm inspired by sparse principal component analysis (sparse-PCA). The key idea of the proposed method is to reformulate the sum-spectral efficiency maximization problem under a group-sparsity constraint into a generalized sparse-PCA problem, which is a tractable non-convex optimization problem. Using this reformulated optimization problem, a computationally efficient algorithm is proposed, which finds the solution that guarantees the first-order necessary optimality condition of the non-convex optimization problem. Simulation results demonstrate significant advantage of the proposed group-sparse beamfroming method.

In this paper, we analyze the information-theoretical limits of a multiple-input single-output (MISO) channel with one-bit transceiver. In particular, we present a capacity expression in a closed form when perfect channel state information (CSI) is available at both a transmitter and a receiver. One major finding is that the capacity-achieving transmission strategy is to uniformly use four multi-dimensional signal points. The four signal points are chosen as a function of the channel and the signal-to-noise ratio (SNR) from the channel input set constructed by a spatial lattice modulation (SLM) method. From our analysis and simulation results, we also demonstrate that the capacity loss caused by the use of one-bit DACs is sufficiently small throughout the entire SNR regime even with a few-bit CSIT feedback that suffices to achieve the capacity.

This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multi-hop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labeled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

A one-bit wireless transceiver is a promising communication architecture that not only can facilitate the design of mmWave communication systems but also can extremely diminish power consumption. The non-linear distortion effects by one-bit quantization at the transceiver, however, change the fundamental limits of communication rates. In this paper, the capacity of a multiple-input single-output (MISO) fading channel with one-bit transceiver is characterized in a closed form when perfect channel state information (CSI) is available at both a transmitter and a receiver. One major finding is that the capacity-achieving transmission strategy is to uniformly use four multi-dimensional constellation points. The four multi-dimensional constellation points are optimally chosen as a function of the channel and the signal-to-noise ratio (SNR) among the channel input set constructed by the spatial lattice modulation method. As a byproduct, it is shown that a few-bit CSI feedback suffices to achieve the capacity. For the case when CSI is not perfectly known to the receiver, practical channel training and CSI feedback methods are presented, which effectively exploit the derived capacity-achieving transmission strategy.

In this letter, we consider the problem of detecting finite-alphabet sparse signals from noisy and coarsely quantized measurements. To solve this problem, we propose a greedy sparse signal detection algorithm referred to as Bayesian matching pursuit (BMP). The key idea of BMP is to identify the non-zero elements of a sparse signal that produce the largest a posteriori probabilities in an iterative fashion. Our simulation results show that the BMP algorithm outperforms the existing sparse signal reconstruction algorithms in terms of frame error rates even with a significantly reduced computational complexity.

In this paper, we propose computationally efficient yet near-optimal soft-output detection methods for coded millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) systems with low-precision analog-to-digital converters (ADCs). The underlying idea of the proposed methods is to construct an extremely sparse inter-symbol-interference channel model by jointly exploiting the delay-domain sparsity in mmWave channels and a high quantization noise caused by low-precision ADCs. Then, we harness this sparse channel model to create a trellis diagram with a reduced number of states and a factor graph with very sparse edge connections, which are used for the computationally efficient soft-output detection methods. Using the reduced trellis diagram, we present a soft-output detection method that computes the log-likelihood ratios (LLRs) of coded bits by optimally combining the quantized received signals obtained from multiple receive antennas using a forward-and-backward algorithm. To reduce the computational complexity further, we also present a low-complexity detection method using the sparse factor graph to compute the LLRs in an iterative fashion based on a belief propagation algorithm. Simulations results demonstrate that the proposed soft-output detection methods provide significant frame-error-rates gains compared with the existing frequency-domain equalization techniques in a coded mmWave MIMO system using one- or two-bit ADCs.

In large-scale antenna systems, using one-bit analog-to-digital converters (ADCs) has recently become important since they offer significant reductions in both power and cost. However, in contrast to high-resolution ADCs, the coarse quantization of one-bit ADCs results in an irreversible loss of information. In the context of channel estimation, studies have been developed extensively to combat the performance loss incurred by one-bit ADCs. Furthermore, in the field of array signal processing, direction-of-arrival (DOA) estimation combined with one-bit ADCs has gained growing interests recently to minimize the estimation error. In this paper, a channel estimator is proposed for one-bit ADCs where the channels are characterized by their angular geometries, e.g., uniform linear arrays (ULAs). The goal is to estimate the dominant channel among multiple paths. The proposed channel estimator first finds the DOA estimate using the maximum inner product search (MIPS). Then, the channel fading coefficient is estimated using the concavity of the log-likelihood function. The limit inherent in one-bit ADCs is also investigated, which results from the loss of magnitude information.

Ahstract-This paper considers massive multiple-input multiple-output (MIMO) downlink system with imperfect channel state information at transmitter (CSIT). A new optimization framework for the joint user-scheduling, power control, and precoding design is presented. The key idea of the proposed optimization framework is to equivalently reformulate an sum-spectral efficiency maximization problem into the maximization problem of the product of Rayleigh quotients. Although this reformulated optimization problem is not convex, the sub-optimal solution that guarantees the first-order optimality condition is found using the proposed general power iteration algorithm. One major observation is that the proposed sub-optimal solutions provide significant gains in terms of the sum-spectral efficiency compared to the conventional user-scheduling algorithm conjunction with zero-forcing precoding for the massive MIMO systems.

This paper presents a low complexity maximum likelihood detection (MLD) algorithm called one-bit-sphere-decoding for an uplink massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs). The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple sub-vectors each with reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the sub-vector and contains a list of sub-codeword vectors. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

This paper considers a single-input multiple-output (SIMO) wide-band communication system that uses low-precision analog-to-digital converters (ADCs) for quantizing the received signal. The key contribution of this paper is to propose an optimal maximum likelihood sequence detector (MLSD), which is referred to as a quantized Viterbi algorithm, extending to the quantized output case of the conventional Viterbi algorithm. In addition, a variant of the quantized Viterbi algorithm is proposed, which is robust to the effect of channel estimation errors. One major observation is that it is possible to achieve low symbol- error-rates in the inter-symbol interference channel even with one-bit ADCs, provided that the number of receive antennas is sufficiently large. Simulations demonstrate that the proposed algorithm is more robust to the channel estimation error than the conventional Viterbi algorithm, which simply treats the channel estimation error as an additional noise.

This paper considers an uplink massive multiple-input multiple-output (MIMO) system with low-precision analog-to-digital converters (ADCs). In this system, a robust maximum-likelihood detection (MLD) method is proposed under imperfect channel state information at a receiver (CSIR). Inspired by reinforcement learning theory, the idea of the proposed method is to enhance the accuracy of a likelihood function estimated at the receiver, by exploiting associations between correctly detected data symbols and quantized received signals. The proposed method utilizes these associations as training examples to learn the true likelihood function of the system, which provides a more accurate estimate for the likelihood function that can overcome the effect of imperfect CSIR. Simulation results show that the proposed MLD considerably outperforms the conventional MLD in terms of the detection performance under imperfect CSIR.

We characterize the ergodic spectral efficiency of a cooperative type of K-tier heterogeneous networks (HetNets) with limited feedback. Specifically, a base station (BS) coordination set is formed by using dynamic clustering across the tiers, wherein the intra-cluster interference is mitigated by using multi-cell zero-forcing based on limited feedback. Modeling the network based on stochastic geometry, we derive analytical expressions for the ergodic spectral efficiency as a function of the system parameters. Leveraging the obtained expression, we formulate a feedback allocation problem and obtain a solution to improve the ergodic spectral efficiency. Simulations show the spectral efficiency improvement by using the proposed feedback allocation. One major finding in the obtained solution is that allocating more feedback to stronger intra-cluster BSs is efficient.

This paper considers an uplink multiuser multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs), in which K users equipped with a single-antenna communicate with one base station (BS) with Nrantennas. In this system, we present a novel multiuser MIMO detection framework inspired by coding theory. The key idea of the proposed framework is to create a code C of length 2Nrover a spatial domain. This code is constructed by an autoencoding function that is completely described by a channel transformation followed by a quantization function of the ADCs for a fixed input constellation set. Using the proposed framework, we present a novel weighted minimum distance decoding (wMDD) that achieves the optimal detection performance by appropriately exploiting unequal channel reliabilities. In addition, we show that bit error rate exponentially decreases with the minimum distance of the code C, which plays a similar role with a condition number in conventional MIMO systems. Furthermore, we develop the communication method that uses the wMDD when the explicit channel state information is not available at the BS. Finally, numerical results are provided to verify the superiority of the proposed method.

This paper considers a K-cell multiple access channel with inter-symbol interference. The primary finding of this paper is that, without instantaneous channel state information at the transmitters, interference-free degrees-of-freedom (DoF) per cell is achievable, provided that the delay spread of the desired links is significantly longer than that of the interfering links when the number of user per cell is sufficiently large. This achievability is shown by a blind interference management method that exploits the relativity in delay spreads between desired and interfering links. In this method, all inter-cell-interference signals are aligned-and-cancelled by using discrete-Fourier-transform-based precoding and combining, both depend only on the lengths of channel-impulse-response. In addition to the DoF analysis, the achievable rate of the proposed method is characterized in a closed-form expression. Some illustrative examples are presented to show an additional sum-DoF gain obtained by exploiting propagation delay in the interfering links or the heterogeneity of the channel coherence time between the desired and interfering links.

In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device networks. The primary finding is that, with the knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both: 1) the minimum of the number of transmit and receive antennas and 2) the density of nodes. This scaling law is achieved by using the multiple transmit antennas to send multiple data streams (e.g., through an openloop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when a successive interference cancellation method is used. Both results require rich scattering environments. A major implication of the derived scaling laws is that, under this scattering assumption, spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in random wireless networks with multiple antennas.

This paper presents a low-complexity near-maximum-likelihood-detection (near-MLD) algorithm called one-bit sphere decoding for an uplink massive multiple-input multiple-output system with one-bit analog-to-digital converters. The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple subvectors each with a reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the subvector and contains a list of subcodeword vectors. The detection performance of the proposed algorithm is also analyzed by characterizing the probability that the proposed algorithm performs worse than the MLD. The analysis shows how the dimension of each sphere and the size of the subcodeword list are related to the performance-complexity tradeoff achieved by the proposed algorithm. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

We characterize the ergodic spectral efficiency of a non-cooperative and a cooperative type ofK-tier heterogeneous network with limited feedback. In the non-cooperative case, a multi-antenna base station (BS) serves a single-antenna user using maximum-ratio transmission based on limited feedback. In the cooperative case, a BS coordination set is formed by using dynamic clustering across the tiers, wherein the intra-cluster interference is mitigated by using multi cell zero-forcing also based on limited feedback. Modeling the network based on stochastic geometry, we derive analytical expressions for the ergodic spectral efficiency as a function of the system parameters. Leveraging the obtained expressions, we formulate feedback partition problems and obtain solutions to improve the ergodic spectral efficiency. Simulations show the spectral efficiency improvement by using the obtained feedback partitions. Our major findings are as follows: 1) in the non-cooperative case, the feedback is only useful in a particular tier if the mean interference is small enough; 2) in the cooperative case, allocating more feedback to stronger intra-cluster BSs is efficient; and 3) in both cases, the obtained solutions do not change depending on the instantaneous signal-to-interference ratio.

This paper proposes spatial lattice modulation (SLM) for multiple-input-multiple-output (MIMO) systems. The key idea of SLM is to jointly exploit spatial, in-phase, and quadrature dimensions to modulate information bits into a multi-dimensional lattice vector. SLM achieves a higher spectral efficiency than the existing spatial modulation and spatial multiplexing methods under the constraint of M-ary pulse-amplitude-modulation (PAM) input signaling per dimension. In particular, it is shown that when the SLM signal set is constructed by using dense lattices, a significant signal-to-noise-ratio (SNR) gain, i.e., a nominal coding gain, is attainable compared to the existing methods. Additionally, a closed-form expression for the average mutual information of generic SLM is derived under Rayleigh-fading environments. Simulations verify the accuracy of the conducted analysis and demonstrate that the proposed SLM techniques achieve higher average mutual information and lower average symbol-vector-error-rate than do existing methods.

This paper considers an uplink multiuser massive multiple-input-multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which K users with a single transmit antenna communicate with one base station (BS) with N receive antennas. In this system, a weighted minimum distance (wMD) decoding was recently proposed by viewing the multiuser MIMO detection problem into an equivalent coding problem with a non-linear channel-dependent code. It was shown that the wMD decoding can outperform the state-of-the-art MIMO detection techniques. Despite its attractive performance, the complexity of the wMD decoding grows exponentially with the number of uplink users, which can prevent from its use in practice. In this paper, we reduce the complexity of the wMD decoding by introducing hierarchical code partitioning. The main idea of the proposed method is that the code is partitioned into the subcodes in a hierarchical manner and using the hierarchical structure, some unnecessary codewords are efficiently removed from the search-space. Then, the wMD decoding is performed over the reduced search-space. Via numerical results, we demonstrate that the proposed method almost achieves the optimal performance of the wMD decoding with a lower decoding complexity.

This paper considers a multi-antenna wireless net- work where the locations of transmitters are distributed as a Poisson point process, which is a natural model for ad-hoc and device-to-device networks. We show that, in such a network, the ergodic spectral efficiency scales linearly with respect to the network density under appropriate multiple antenna configurations and diversity assumptions. This scaling law is achieved by a simple zero-forcing decoder, which eliminates inter-stream interference using spatial multiplexing transmissions. We also show that when each receiver knows channel state information from some interferers, a higher scaling law holds for ergodic spectral efficiency than that without channel state information when using a partial zero-forcing method which eliminates dominant interference signals while boosting the desired signal power. Further, we show that spatial multiplexing transmission methods are essential for obtaining better scaling laws in certain regions of network parameters.

We investigate secure degrees of freedom (SDoF) of a single-input single-output (SISO) wiretap channel with a single helper without channel state information at the transmitters (CSIT). Wireless communication systems inherently suffer from intersymbol interference (ISI) due to channel dispersion. In this paper, we propose a novel blind cooperative jamming scheme that exploits the ISI heterogeneity to achieve positive SDoF, even without any CSIT. In order to achieve positive SDoF, the proposed approach only requires statistical properties of the ISI channel. In particular, we show that if LB is the effective ISI channel multipath link length towards the legitimate receiver (Bob) and LE is the link length towards the eavesdropper (Eve), a positive SDoF of LB-LE is achievable. To the best of our 2(LB -1) knowledge, this is the first work that exploits ISI link length heterogeneity to achieve positive secure degrees of freedom.

This paper considers a multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we present a new MIMO detection approach using coding theory. The principal idea of the proposed approach is to transform a non-linear MIMO channel to a linear MIMO channel by leveraging both a p-level quantizer and a lattice code where p ≥ 2. After transforming to the linear MIMO channel with the sets of finite input and output elements, efficient MIMO detection methods are proposed to attain both diversity and multiplexing gains by using algebraic coding theory. In particular, using the proposed methods, the analytical characterizations of achievable rates are derived for different MIMO configurations. One major observation is that the proposed approach is particularly useful for a large MIMO system with the ADCs that use a few bits.

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.

This paper considers a K-user single-input-single-output interference channel with inter-symbol interference (ISI), in which the channel coefficients are assumed to be linear time-invariant with finite-length impulse response. The primary finding of this paper is that, with no channel state information at a transmitter (CSIT), the sum-spectral efficiency can be made to scale linearly with K, provided that the desired links have longer impulse response than do the interfering links. This linear gain is achieved by a novel multi-carrier communication scheme which we call interference-free orthogonal frequency division multiplexing (IF-OFDM). A major implication of the derived results is that separate encoding across subcarriers per link is sufficient to linearly increase the sum-spectral efficiency with K in the interference channel with ISI. Simulation results support this claim.

This paper considers a K-cell multiple access channel with inter-symbol interference (ISI). The primary finding of this paper is that, without instantaneous channel state information at a transmitter, the interference-free sum degrees of freedom of K is asymptotically achievable when the number of users per cell is sufficiently large, and also when the number of channel-impulse-response taps of desired links is greater than that of interfering links. This achievability is shown by a blind interference management method that exploits the relativity in delay spreads between desired and interfering links.

This paper investigates an uplink multiuser massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which K users with a single-antenna communicate with one base station (BS) with nrantennas. In this system, we propose a novel MIMO detection framework, which is inspired by coding theory. The key idea of the proposed framework is to create a non-linear code C of length nrand rate K/nrusing the encoding function that is completely characterized by a non-linear MIMO channel matrix. From this, a multiuser MIMO detection problem is converted into an equivalent channel coding problem, in which a codeword of the C is sent over nrparallel binary symmetric channels, each with different crossover probabilities. Levereging this framework, we develop a maximum likelihood decoding method, and show that the minimum distance of the C is strongly related to a diversity order. Furthermore, we propose a practical implementation method of the proposed framework when the channel state information is not known to the BS. The proposed method is to estimate the code C at the BS using a training sequence. Then, the proposed weighted minimum distance decoding is applied. Simulations results show that the proposed method almost achieves an ideal performance with a reasonable training overhead.

In this paper, we propose a novel relay-aided successive aligned interference cancellation (RaSAIC) method for a 2 × K X channel with L full-duplex relays. The key idea behind the RaSAIC is to apply a cooperative relays' precoding technique that aligns interference signals at unintended receivers while creating an Alamouti structure for the desired signals. With the proposed cooperative relay transmission technique, it is shown that both the optimal degrees of freedom gain of 2K/K+1 and the diversity gain of two are achievable for constant channels when the numbers of relays' antennas are enough to satisfy the derived feasibility conditions without requiring channel state information (CSI) at transmitters but with global and local CSI at relays and receivers, respectively.

In this paper, we propose coded compressive sensing that recovers an n-dimensional integer sparse signal vector from a noisy and quantized measurement vector whose dimension m is far-fewer than n. The core idea of coded compressive sensing is to construct a linear sensing matrix whose columns consist of lattice codes. We present a two-stage decoding method named compute-and-recover to detect the sparse signal from the noisy and quantized measurements. In the first stage, we transform such measurements into noiseless finite-field measurements using the linearity of lattice codewords. In the second stage, syndrome decoding is applied over the finite-field to reconstruct the sparse signal vector. A sufficient condition of a perfect recovery is derived. Our theoretical result demonstrates an interplay among the quantization level p, the sparsity level k, the signal dimension n, and the number of measurements m for the perfect recovery. Considering 1-bit compressive sensing as a special case, we show that the proposed algorithm empirically outperforms an existing greedy recovery algorithm.

We consider a multi-antenna downlink cellular network using either single-user maximal ratio transmission (MRT) or multi-user zero-forcing (ZF) transmission. The locations of the base stations are modeled by a Poisson point process to allow the inter-cell interference to be tractably analyzed. A tight lower bound on the optimum number of feedback bits maximizing the net spectral efficiency is derived, whereby the cost of feedback sent via uplink is subtracted from the corresponding gain in downlink spectral efficiency. When using MRT, the optimum number of feedback bits is shown to scale linearly with the number of antennas, and logarithmically with the channel coherence time. With ZF, the optimum amount of feedback scales the same as with MRT, but additionally also increases linearly with the pathloss exponent.

This paper proposes a novel multi-antenna relay-aided interference management technique that can use imperfect channel knowledge for interference relay channels. Using the proposed method, it is shown that KM/K+M-1 degrees of freedom (DoF) are achievable in a K-user multiple-input-single-output interference relay channel when the relay has M antennas with a certain type of limited channel knowledge. By leveraging this result, it is demonstrated that the interference-free DoF of K are asymptotically achieved as M approaches infinity. One major implication of these results is that even under this limited channel knowledge, the use of massive antennas at the relay is sufficient to recover the optimal DoF for relay-aided interference networks with perfect channel knowledge.

This paper proposes a method for designing base station (BS) clusters and cluster patterns for pair-wise BS coordination. The key idea is that each BS cluster is formed by using the second-order Voronoi region, and the BS clusters are assigned to a specific cluster pattern by using edge-coloring for a graph drawn by Delaunay triangulation. The main advantage of the proposed method is that the BS selection conflict problem is prevented, while users are guaranteed to communicate with their two closest BSs in any irregular BS topology. With the proposed coordination method, analytical expressions for the rate distribution and the ergodic spectral efficiency are derived as a function of relevant system parameters in a fixed irregular network model. In a random network model with a homogeneous Poisson point process, a lower bound on the ergodic spectral efficiency is characterized. Through system level simulations, the performance of the proposed method is compared with that of conventional coordination methods: dynamic clustering and static clustering. Our major finding is that, when users are dense enough in a network, the proposed method provides the same level of coordination benefit with dynamic clustering to edge users.

This paper considers large random wireless networks, where transmit-and-receive node pairs communicate within a certain range while sharing a common spectrum. By modeling the spatial locations of nodes as a Poisson point process, analytical expressions for the ergodic spectral efficiency of a typical node pair are derived as a function of the channel state information available at a receiver (CSIR) in terms of relevant system parameters: the density of communication links, the number of receive antennas, the path loss exponent, and the operating signal-to-noise ratio. One key finding is that when the receiver only exploits CSIR for the direct link, the sum spectral efficiency increases linearly with the density, provided the number of receive antennas increases as a certain superlinear function of the density. When each receiver exploits CSIR for a set of dominant interfering links in addition to that of the direct link, the sum spectral efficiency increases linearly with both the density and the path loss exponent if the number of antennas is a linear function of the density. This observation demonstrates that having CSIR for dominant interfering links provides an order gain in the scaling law. It is also shown that this linear scaling holds for direct CSIR when incorporating the effect of the receive antenna correlation, provided that the rank of the spatial correlation matrix scales superlinearly with the density. These scaling laws are derived from integral representations of the distribution of the signal to interference and noise ratio, which are of independent interest and which in turn derived from stochastic geometry and more precisely from the theory of shot noise fields. Simulation results back the scaling laws and the integral representations.

We consider a downlink cellular network where multi-antenna base stations (BSs) transmit data to single-antenna users by using one of two linear precoding methods with limited feedback: 1) maximum ratio transmission (MRT) for serving a single user or 2) zero forcing (ZF) for serving multiple users. The BS and user locations are drawn from a Poisson point process, allowing expressions for the signal-to-interference coverage probability and the ergodic spectral efficiency to be derived as a function of system parameters, such as the number of BS antennas and feedback bits, and the pathloss exponent. We find a tight lower bound on the optimum number of feedback bits to maximize the net spectral efficiency, which captures the overall system gain by considering both of downlink and uplink spectral efficiency using limited feedback. Our main finding is that, when using MRT, the optimum number of feedback bits scales linearly with the number of antennas, and logarithmically with the channel coherence time. When using ZF, the feedback scales in the same ways as MRT, but also linearly with the pathloss exponent. The derived results provide system-level insights into the preferred channel codebook size by averaging the effects of short-term fading and long-term pathloss.

This paper characterizes the performance of multiuser joint transmission (MU-JT) with pair-wise dynamic base station (BS) clustering. For analyzing the performance of such BS cooperation method, a tractable model is presented by means of stochastic geometry. Using tools of stochastic geometry, a tight lower bound of the instantaneous signal-to-interference ratio (SIR) distribution is derived in a closed form in terms of relevant system parameters: the path-loss exponent and the topologies of users in the BS cooperative region. Our key finding is that the pair-wise dynamic BS cooperation through MU-JT provides a better rate coverage performance than that of single-user joint transmission (SU-JT) over the entire range of the rate threshold when each user is close enough to the associated BS. Through simulations, the exactness of the derived analytical expression is verified.

In this paper, we propose a new retrospective interference alignment for two-cell multiple-input multiple-output (MIMO) interfering multiple access channels (IMAC) with the delayed channel state information at the transmitters (CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF compared to the case of no CSIT. The key idea is to align multiple interfering signals from adjacent cells onto a small dimensional subspace over time by fully exploiting the previously received signals as side information with outdated CSIT in a distributed manner. Remarkably, we show that the retrospective interference alignment can achieve the optimal sum-DoF in the context of two-cell two-user scenario by providing a new outer bound.

A space-time physical-layer network coding (ST-PNC) method is presented for information exchange among multiple users over fully connected multiway relay networks. The method involves two steps: 1) side-information learning and 2) space-time relay transmission. In the first step, different sets of users are scheduled to send signals over networks, and the remaining users and relays overhear the transmitted signals, thereby learning the interference patterns. In the second step, multiple relays cooperatively send out linear combinations of signals received in the previous phase using space-time precoding so that all users efficiently exploit their side information in the form of 1) what they sent and 2) what they overheard in decoding. This coding concept is illustrated through two simple network examples. It is shown that ST-PNC improves the sum of degrees of freedom (sum-DoF) of the network compared to existing interference management methods. With ST-PNC, the sum-DoF of a general multiway relay network without channel knowledge at the users is characterized in terms of relevant system parameters, chiefly the number of users, the number of relays, and the number of antennas at relays. A major implication of the derived results is that efficiently harnessing both transmitted and overheard signals as side information brings significant performance improvements to fully connected multiway relay networks.

This paper proposes a random network model for a device-to-device (D2D) underlaid cellular system using stochastic geometry and develops centralized and distributed power control algorithms. The goal of centralized power control is twofold: ensure that the cellular users have sufficient coverage probability by limiting the interference created by underlaid D2D users, while scheduling as many D2D links as possible. For the distributed power control method, the optimal on-off power control strategy is proposed, which maximizes the sum rate of the D2D links. Expressions are derived for the coverage probabilities of cellular, D2D links, and the sum rate of the D2D links in terms of the density of D2D links and the path-loss exponent. The analysis reveals the impact of key system parameters on the network performance. For example, the bottleneck of D2D underlaid cellular networks is the cross-tier interference between D2D links and the cellular user, not the D2D intratier interference when the density of D2D links is sparse. Simulation results verify the exactness of the derived coverage probabilities and the sum rate of D2D links.

This paper characterizes the performance of coordinated beamforming with dynamic clustering. A downlink model based on stochastic geometry is put forth to analyze the performance of such a base station (BS) coordination strategy. Analytical expressions for the complementary cumulative distribution function (CCDF) of the instantaneous signal-to-interference ratio (SIR) are derived in terms of relevant system parameters, chiefly the number of BSs forming the coordination clusters, the number of antennas per BS, and the pathloss exponent. Utilizing this CCDF, with pilot overheads further incorporated into the analysis, we formulate the optimization of the BS coordination clusters for a given fading coherence. Our results indicate that: 1) coordinated beamforming is most beneficial to users that are in the outer part of their cells yet in the inner part of their coordination cluster and that 2) the optimal cluster cardinality for the typical user is small and it scales with the fading coherence. Simulation results verify the exactness of the SIR distributions derived for stochastic geometries, which are further compared with the corresponding distributions for deterministic grid networks.

This paper proposes an interference alignment method with distributed and delayed channel state information at the transmitter (CSIT) for a class of interference networks. The core idea of the proposed method is to align interference signals over time at the unintended receivers in a distributed manner. With the proposed method, achievable tradeoffs between the sum of degrees of freedom (sum-DoF) and feedback delay of CSI are characterized in both the X-channel and three-user interference channel to reveal the impact on how the CSI feedback delay affects the sum-DoF of the interference networks. A major implication of derived results is that distributed and moderately delayed CSIT is useful to strictly improve the sum-DoF over the case of no CSI at the transmitter in a certain class of interference networks. For a class of X-channels, the results show how to optimally use distributed and moderately delayed CSIT to yield the same sum-DoF as instantaneous and global CSIT. Furthermore, leveraging the proposed transmission method and the known outer bound results, the sum-capacity of the two-user X-channel with a particular set of channel coefficients is characterized within a constant number of bits.

In this paper, a space-time physical-layer network coding method is proposed in fully-connected multi-way relay networks. The proposed method includes two essential steps: side-information learning; and space-time relay transmission. In the phase of side-information learning, sets of users are scheduled to transmit messages over networks and the remaining users and relays overhear the transmitted messages to learn the interference patterns. In the phase of space-time relay transmission, multiple relays cooperatively transmit linear combinations of received messages in the previous phase using space-time precoding so that all users simultaneously harness their side-information in decoding. With this transmission technique, it is shown that the sum of degrees of freedom of the network is improved compared to existing interference management methods. Our key finding is that efficiently exploiting both transmitted and overheard messages as side-information brings substantial performance gains to fully-connected multi-way relay networks.

This paper characterizes the performance of coordinated beamforming with dynamic clustering. A downlink cellular model based on stochastic geometry is put forth to analyze the performance of such base station (BS) coordination strategy. Analytical expressions for the complementary cumulative distribution function (CCDF) of the instantaneous signal-to-interference ratio (SIR) are derived in terms of relevant system parameters, chiefly the number of coordinated BSs, the number of antennas, and the path-loss exponent. Utilizing this CCDF, with pilot overheads further incorporated into the analysis, we formulate the computation of the number of coordinated BSs that maximizes the spectral efficiency for a given fading coherence. The results make precise the intuition that the slower the fading, the more beneficial the coordination. Simulation results verify the exactness of the SIR distribution derived for stochastic geometries, which is further compared with the corresponding distribution for a deterministic grid model.

This paper studies a network information flow problem for a multiple-input multiple-output (MIMO) Gaussian wireless network with K users and a single intermediate relay having M antennas. In this network, each user sends a multicast message to all other users while receiving K-1 independent messages from the other users via an intermediate relay. This network information flow is termed a MIMO Gaussian K-way relay channel. For this channel, it is shown that the optimal sum degrees of freedom (sum-DoF) is KM/K-1, assuming that all nodes have global channel knowledge and operate in full-duplex. A converse argument is derived by cut-set bounds. The achievability is shown by a repetition coding scheme with random beamforming in encoding and a zero-forcing method combined with self-interference cancelation in decoding. Furthermore, under the premise that all nodes have local channel state information at the receiver only and operate in half-duplex mode, it is shown that a total K/2 DoF is achievable when M=K-1. The key to showing this result is a novel encoding and decoding scheme, which creates a set of network code messages with a chain structure during the multiple access phase and performs successive interference cancelation using side-information for the broadcast phase. One major implication of the derived results is that efficient exploitation of the transmit message as side-information leads to an increase in the sum-DoF gain in a multiway relay channel with multicast messages.

This paper characterizes the degree-of-freedom (DoF) regions for the multiuser vector broadcast channel with periodic channel state information (CSI) feedback. As a part of the characterization, a new transmission method called space-time interference alignment is proposed, which exploits both the current and past CSI jointly. Using the proposed alignment technique, an inner bound of the sum-DoF region is characterized as a function of a normalized CSI feedback frequency, which measures CSI feedback speed compared to the speed of user's channel variations. One consequence of the result is that the achievable sum-DoF gain is improved significantly when a user sends back both current and outdated CSI compared to the case where the user sends back current CSI only. Then, a tradeoff between CSI feedback delay and the sum-DoF gain is characterized for the multiuser vector broadcast channel in terms of a normalized CSI feedback delay that measures CSI obsoleteness compared to channel coherence time. A crucial insight is that it is possible to achieve the optimal DoF gain if the feedback delay is less than a derived fraction of the channel coherence time. This precisely characterizes the intuition that a small delay should be negligible.

This paper considers a fully-connected interference network with a relay in which multiple users equipped with a single antenna want to exchange multiple unicast messages with other users in the network by sharing the relay equipped with multiple antennas. For such a network, the optimal degrees of freedom (DoF) are derived by providing both converse and achievability. Further, considering single-antenna relays in the three-user fully-connected interference network, it is shown that three distributed relays with a single antenna is sufficient to achieve the optimal DoF. A major implication of the derived DoF results is that a relay with multiple antennas or distributed relays employing a single antenna increases the capacity scaling law of the multi-user interference network when multiple directional information flows are considered, even if the networks are fully-connected and all nodes operate in half-duplex. These results verify the intuition that the relay is useful in increasing DoF for the multi-way the interference network.

A conventional relay causes a processing delay of at least one symbol duration relative to the direct paths between a source and a destination. This delay caused by using the conventional relay limits the degrees of freedom (DoF) gain in a wireless interference network. In this paper, it is shown that the exploitation of an instantaneous relay (relay-without-delay) improves DoF gain significantly for a two-user wireless network. Specifically, for two different message settings: the interference channel and the X channel, it is shown that a total of 3/2 and 5/3 DoF are achievable almost surely, respectively. The achievable schemes are inspired by the notion of aligned interference neutralization recently proposed for the layered two-user two-hop interference channel.

This paper considers the two-cell two-hop multiple-input-multiple-output (MIMO) interference channel, where two source groups consisting of multiple users with a single antenna wish to communicate with two multiantenna destinations by sharing two multiantenna relays. For such a channel, an inner bound on the degrees of freedom is derived for different channel knowledge assumptions and relay operations. Assuming global channel knowledge at the relays and full-duplex relay operation, it is shown that two cascaded interfering links can be decomposed into two independent parallel relay channels while sharing the spectrum. The key to showing this result is a novel amplify-and-forward interference-free relay transmission method, which performs interference-shaping during reception and interference neutralization during transmission. Assuming that the relays have global channel knowledge only for the first hop, a spectrally efficient relaying protocol is proposed that overcomes the loss due to half-duplex relaying constraint. The proposed protocol improves performance compared to a trivial time-division-multiple access method for the two-cell two-hop MIMO interference channel.

In this paper, we study new network information flow called multiple-input multiple-output (MIMO) two-way relay interference channels where two links of relay systems are interfering with each other. In this system, we characterize the achievable total degrees of freedom (DOF) when all user nodes and relays have M and N antennas, respectively. We provide three different methods, namely, time-division multiple access, signal space alignment for network coding (SSANC), and a new interference neutralization (IN) scheme. In the SSA-NC scheme, one relay is selected to fully exploit the dimension of the chosen relay for network coding. For the IN, we propose a new relay transmission scheme where two relays cooperatively design the beamforming vectors so that the interference signals are neutralized at each receiver. By adopting three different relaying strategies, we show that the DOF of max {min(4N, 2M), min(2N, 2⌊4/3M⌋), min(2N - 1, 4M)} is achieved for MIMO two-way relay interference channels.

In this work, we propose a simple yet effective beam-forming technique for multiple-input multiple-output (MIMO) multi-pair two-way relay channels. Two key ingredients in our technique are adoption of signal space alignment (SSA) for transmit and receive beamforming and amplify-and-forward (AF) relay beamforming based on advanced zero-forcing (ZF) criterion. From the sum-rate analysis on MIMO multi-pair two-way relay channels, we show that the proposed method achieves full multiplexing gain and substantial beamforming gain in realistic multi-pair two-way relay scenario.

We derive a new sum-rate lower bound of the multiuser multi-input multi-output (MU-MIMO) cellular two-way relay channel (cTWRC) which is composed of a base station (BS) and a relay station (RS), both with multiple antennas, and non-cooperative mobile stations (MSs), each with a single antenna. In the first phase, we show that network coding based on decode-and-forward relaying can be generalized to arbitrary input cardinality through proposed lattice code-aided linear precoding, despite the fact that precoding is permitted only at the BS due to non-cooperation among the MSs. In addition, a new sum-rate lower bound for the second phase is derived by showing that the two spatial decoding orders at the BS and MSs for one-sided zero-forcing dirty-paper-coding must be identical. From the fundamental gain of network coding, our sum-rate lower bound achieves the full multiplexing gain regardless of the number of antennas at the BS or RS, and strictly exceeds the previous lower bound which is based on traditional multiuser decoding in the first phase. Furthermore, it is shown that our lower bound asymptotically achieves the sum-rate upper bound in the presence of signal-to-noise ratio (SNR) asymmetry in high SNR regime, and sufficient conditions for this SNR asymmetry are drawn.

Accurate channel direction information is essential to achieve considerable capacity gains in multiple-input multiple-output (MIMO) wireless communication systems. Limited feedback using a polar-cap differential codebook which utilizes the temporal correlation in multiple-input single-output (MISO) channels is presented in this paper. We first describe the general properties of the polar-cap differential codebook and then explain the design methodology of the size of the polar-cap given the temporal correlation coefficient. We also propose an enhancement of the polar-cap differential codebook which is suitable for a spatially correlated channel. We compare the polar-cap differential codebook with a rotation-based differential codebook in terms of the chordal distance to demonstrate the superiority of the polar-cap differential codebook. Monte Carlo simulation results show that the polar-cap differential codebook facilitates a significant performance gain in both temporally and spatially correlated channels.

In this paper, we consider K-user Y channels where K users simultaneously exchange messages with each other via an intermediate relay. Degrees of freedom (DOF) of Y channels with multiple antennas is not known in general. Investigation of the feasibility conditions of signal space alignment for network coding is an initial step for addressing this open problem. We verify that when user i with M-i antennas sends K-1 independent messages to the other users through a relay with N antennas and each message achieves the DOF of d, the total DOF of dK(K-1) is attained if M-i >= d(K - 1), N >= dK(K-1)/2 and N < min {M-i + M-j - d vertical bar for all i not equal j}. It is accomplished by adopting the signal space alignment for the network coding during both the multiple access phase and the broadcast phase. It is shown that the proposed scheme obtains not only a network coding gain but also an alignment gain in terms of the normalized DOF, as K ->infinity. Also for Y channels where all nodes have a single antenna, we show that the DOF of 2 is achieved regardless of the number of users by using the rational dimension framework.

This paper investigates a network information flow problem for a multiple-input multiple-output (MIMO) Gaussian wireless network with K-users and a single intermediate relay having M antennas. In this network, each user intends to convey a multicast message to all other users while receiving K-1 independent messages from the other users via an intermediate relay. This network information flow is termed a MIMO Gaussian K-way relay channel. For this channel, we show that K/2 degrees of freedom is achievable if M=K-1. To demonstrate this, we come up with an encoding and decoding strategy inspired from cryptography theory. The proposed encoding and decoding strategy involves a signal space alignment for an encryption message for the multiple access phase (MAC) and zero forcing with successive network code decoding for the broadcast (BC) phase. The idea of the signal space alignment for an encryption message is that all users cooperatively choose the precoding vectors to transmit the message so that the relay can receive a proper encryption message with a special structure, network code chain structure. During the BC phase, zero forcing combined with successive network code decoding enables all users to decipher the encryption message from the relay despite the fact that they all have different self-information which they use as a key.

In this paper, we consider K-user MIMO Y channels where K users simultaneously exchange messages with each other via an intermediate relay. Degrees of freedom (DOF) of this channel are not known in general. The investigation of the feasibility conditions of signal space alignment for network coding is an initial step for addressing this open problem. We verify that when user i has Miantennas (i = 1, ⋯, K) and a relay has N antennas, the DOF of K(K - 1) is achieved if Mi≥ K - 1, N ≥ (K(K-1)/2) and Ni+ Mj|∀ i ≠ j}. It is accomplished by adopting the signal space alignment for network coding during both multiple access channel and broadcasting channel phase. The achievability is shown by an amplify-and forward (AF) relaying strategy combined with the signal space alignment for the network coding scheme, which provides a lower bound of the capacity.

In this paper, we study a two cell multiple input single-output interfering broadcast channel with finite rate feedback. In this system, we first derive the rate loss due to the quantization error by considering a coordinated zero-forcing beamforming. In addition, feedback bits allocation methods are proposed to minimize the performance degradation caused by the quantization error. Lastly, we investigate how many feedback bits per user are necessary to maintain the optimal multiplexing gain in MISO-IFBC. Through numerical evaluations, we show that our proposed feedback bits allocation strategy provides significant gain compared to a trivial bits allocation scheme.

In this paper, we study a K-cell multiple input single-output interfering broadcast channel (MISO-IFBC) with finite rate feedback. In this channel, we first derive the rate loss due to the quantization error by considering both a coordinated zero-forcing beamforming and random vector quantization method. Using this result, feedback bits allocation methods are proposed to minimize the performance degradation in K-cell MISO-IFBC. Furthermore, we investigate how many feedback bits per user are necessary to maintain the optimal multiplexing gain in K-cell MISO-IFBC. Through numerical evaluations, we show that our proposed feedback bits allocation strategy provides significant gain compared to a trivial bits allocation scheme.

The interference alignment (IA) is a promising technique to effectively mitigate interferences in wireless communication systems. To show the potential benefits of such an IA scheme, this letter focuses on a two-cell multiple-input multiple-output (MIMO) Gaussian interfering broadcast channels (MIMO-IFBC) with M transmit antennas and N receive antennas. It corresponds to a downlink scenario for cellular networks with two base stations (BSs) with M transmit antennas per BS, and two users with N receive antennas per user, on the cell-boundary of each BS. In this scenario, we propose a novel IA technique jointly designing transmit and receive beamforming vectors in a closed-form expression without iterative computation. It is also analytically shown that the proposed IA algorithm achieves the optimal degrees of freedom (DoF) of 2N in the case of [¾N] ≤ M <; 2N. The simulations demonstrate that not only the analytical results are valid, but the sum-rate of our proposed scheme also outperforms those of conventional techniques, especially in the high signal-to-noise ratio (SNR) regime.

In this paper, we study a network information flow problem for a multiple-input-multiple-output (MIMO) Gaussian wireless network with three users each equipped with M antennas and a single intermediate relay equipped with N antennas. In this network, each user intends to convey independent messages for two different users via the intermediate relay while receiving two independent messages from the other two users. This is a generalized version of the two-way relay channel for the three-user case. We will call it a "MIMO Y channel." For this MIMO Y channel, we show that the capacity is 3M log(SNR) + o(log(SNR)) if N ≥ ⌈3M/2⌉ by using two novel signaling techniques, which are signal space alignment for network coding, and network-coding-aware interference nulling beamforming.

In this paper, we study degrees of freedom for the K-User multiple-input multiple-output (MIMO)-interference channel (IFC) with constant channel coefficients. In this channel, we investigate how many total number of transmit antennas, M1+ M2+ ¿ + MK, are required in minimum to achieve di= 1,¿i degrees of freedom when all receivers have N = 2 antennas, which is a downlink communication scenario. To answer this question, we propose a new interference alignment scheme based on intersection subspace property of the vector space. The proposed interference alignment scheme can be easily generalized regardless of the number of users. In addition, we investigate degrees of freedom for the partially connected MIMOIFC where some arbitrary interfering links are disconnected due to the large path loss or deep fades. In this channel model, we examine how these disconnected links are considered on designing the beamforming vectors for interference alignment.

In this paper, we study a new network information flow for a multiple-input multiple-output (MIMO) wireless network system with three users and a single intermediate relay which of each is equipped with multiple antennas. In this system, each user wants to convey independent messages for different two users via the intermediate relay while receiving two independent messages from the other two users by using only two time slots. This network information flow is a generalized version of the two-way relay channel for more than two users case. We will call this network information flow as a dasiaMIMO Y channel.psila To achieve much higher multiplexing gain in the MIMO Y channel, we propose two novel signaling techniques, which are signal space alignment for network coding during the first time slot and network coding based interference nulling beamforming during the second time slot. By evaluating the multiplexing gain it is shown that the proposed signaling scheme significantly outperforms the conventional time division multiple access and multi-user MIMO schemes.

A relay based wireless network system is basically composed of three nodes, two source nodes which are also destination nodes and the relay node which helps reliable communications between two nodes. The amplify-and-forward (AF) relaying scheme is more convenient to implement than the decode-and-forward (DF) relaying scheme which requires a decoding process at the relay node. In this paper, we design an optimal linear precoder and decoder which minimize the sum of mean squared error (SMSE) when the relay and the destination node have perfect knowledge of the channel state information (CSI) for the forward and the backward channels, respectively. The optimal linear precoder and decoder solutions are obtained by using the proposed iterative algorithm which satisfies the KKT optimality conditions. The average BER performance of the proposed scheme is compared with those of a conventional AF-ZF scheme and AF-MMSE scheme.