Decoding Vectors Research Articles

Joint data power control and LSFD design in distributed cell-free massive MIMO under non-ideal UE hardware

This paper investigates distributed cell-free massive multiple-input multiple-output with non-ideal user equipment hardware under spatially correlated channels. By employing the use-and-then-forget technique, a lower capacity bound is derived based on the established generalized UE hardware impairments model. In addition, maximum ratio combining can be used to derive a closed-form expression of the spectral efficiency (SE), which offers novel insights into the impact of non-ideal UE hardware on network performance. Furthermore, a max–min SE fairness problem with UE hardware impairments is established where the optimization variables are data power and large-scale fading decoding (LSFD) vectors. Since this is a non-convex problem, we devise an iterative alternating optimization algorithm based on the bisection search to acquire the globally optimal solution. Numerical results indicate that the recommended joint data power control and LSFD design algorithm provides higher SE for the weakest UE, thus significantly enhancing the total SE of the network.

Over-the-Air Computation With Multiple Receivers: A Space-Time Approach

The emerging concept of Over-the-Air (OtA) computation has shown great potential for achieving resource-efficient data aggregation across large wireless networks. However, current research in this area has been limited to the standard many-to-one topology, where multiple nodes transmit data to a single receiver. In this study, we address the problem of applying OtA computation to scenarios with multiple receivers, and propose a novel communication design that exploits joint precoding and decoding over multiple time slots. To determine the optimal precoding and decoding vectors, we formulate an optimization problem that aims to minimize the mean squared error of the desired computations while satisfying the unbiasedness condition and power constraints. Our proposed multi-slot design is shown to be effective in saving communication resources (e.g., time slots) and achieving smaller estimation errors compared to the baseline approach of separating different receivers over time.

Joint Power Control and LSFD for Wireless-Powered Cell-Free Massive MIMO

This paper considers wireless uplink information and downlink power transfer in cell-free massive multiple-input multiple-output systems. The single-antenna user equipments (UEs) utilize the energy harvested in the downlink to transmit uplink pilot and information signals to the multiple-antenna access points (APs). We consider Rician fading and maximum ratio processing based on either linear minimum mean-squared error (LMMSE) or least-squares (LS) channel estimation. We derive the average harvested energy by using a practical non-linear energy harvesting circuit model for both coherent and non-coherent transmission schemes. Furthermore, the uplink spectral efficiency (SE) is derived for all the considered methods and the max-min fairness problem is cast where the optimization variables are the AP and UE power control coefficients together with the large-scale fading decoding vectors. The objective is to maximize the minimum SE of the UEs’ under APs’ and UEs’ transmission power constraints. A novel alternating optimization algorithm with guaranteed convergence and improvement at each step is proposed to solve the highly-coupled non-convex problem.

Is Massive MIMO Robust Against Distributed Jammers?

In this paper, we evaluate the uplink spectral efficiency (SE) of a single-cell massive multiple-input-multiple-output (MIMO) system with distributed jammers. We define four different attack scenarios and compare their impact on the massive MIMO system as well as on a conventional single-input-multiple-output (SIMO) system. More specifically, the jammers attack the base station (BS) during both the uplink training phase and data phase. The BS uses either least squares (LS) or linear minimum mean square error (LMMSE) estimators for channel estimation and utilizes either maximum-ratio-combining (MRC) or zero-forcing (ZF) decoding vectors. We show that ZF gives higher SE than MRC but, interestingly, the performance is unaffected by the choice of the estimators. The simulation results show that the performance loss percentage of massive MIMO is less than that of the SIMO system. Moreover, we consider two types of power control algorithms: jamming-aware and jamming-ignorant. In both cases, we consider the max-min and proportional fairness criteria to increase the uplink SE of massive MIMO systems. We notice numerically that max-min fairness is not a good option because if one user is strongly affected by the jamming, it will degrade the other users’ SE as well. On the other hand, proportional fairness improves the sum SE of the system compared with the full power transmission scenario.

Interference alignment with iterative channel estimation for the reciprocal [formula omitted] MIMO X Network

This paper investigates an interference alignment (IA) scheme for a reciprocal multi-input multi-output (MIMO) M×2 X network where the knowledge of channel state information (CSI) is required. In our proposed approach, singular vectors, calculated from the singular value decomposition (SVD) of channel matrices, are used to compute precoding and zero-forcing (ZF) decoding matrices at transmitters and receivers, respectively. The orthogonality between precoding and decoding vectors that results from SVD is an advantage for realizing IA scheme because we can rely on an iterative scheme, known as iterative power method (IPM). The singular vectors resulting from the IPM approach converge to the actual ones after multiple iterations assuming a common “virtually static” channel between each link. However, due to the fast fading nature of the channel, computed precoding and ZF decoding vectors will be different from those resulting from the SVD of the actual channel. To this end, the IPM applied to get an estimate of precoding and ZF decoding vectors allows a better tracking of the time-varying channel. The bit error rate of the proposed scheme is evaluated by means of Monte Carlo simulations and compared with that achieved by a perfect CSI based system.

Interference Alignment and Cancelation for the Uplink of Heterogeneous Networks

In this paper, we propose an interference alignment and cancelation (IAC) scheme for the uplink of heterogeneous networks (HetNets). Due to the random deployment of small cells and the heterogeneous deployment of macrocells and small cells, complex interference occurs in HetNets. This makes the optimal interference cancelation order and the corresponding interference alignment (IA) scheme of IAC hard to determine. To address this problem, we first develop a set of constraints that should be satisfied by users, base stations, and interference cancelation when IAC is carried out in the HetNets. Meanwhile, by giving the design method of the precoding and decoding vectors, we prove that IAC is feasible if the developed constraints are satisfied. Then, to determine the optimal IAC scheme, based on the developed constraints, we construct an optimization problem with the aim of maximizing the number of data streams that can be transmitted in the network. By solving the optimization problem, the optimal interference cancelation order and the corresponding IA scheme can be obtained. The simulation results show that the proposed IAC scheme can significantly increase the number of data streams transmitted in the whole network.

Throughput Analysis of Interference Alignment for a General Centralized Limited Feedback Model

In this paper, we consider a general centralized feedback model for interference alignment (IA) algorithms of which precoders and decoders are designed with quantized channel state information (CSI) at a central unit and then fed back to corresponding transmitters and receivers via finite-rate links. The significant difference of this model from existing models is that all the realizations of channels, precoders, and decoders are fed back by finite rates. The signal-to-interference-plus-noise ratio (SINR) characteristic of this model is analyzed, which helps derive an upper bound of average throughput loss relative to perfect IA. The upper bound is shown to be tight by simulation, particularly in the high-feedback regime. Based on this result, the scaling laws of feedback bits for channels, precoding, and decoding vectors to obtain full degree of freedom are derived, which are backward compatible to the existing results for quantized CSI only or quantized precoder only. Simulation results show that the system is interference limited at a high signal-to-noise ratio (SNR) if the three scaling laws are not simultaneously satisfied, and the throughput performance is dominantly limited by the worst feedback links among the channels, precoders, and decoders.

A Novel Algorithm for Improving the SINR of D2D Communication in the Cellular Network

In this paper, we investigate the interference scenario where multiple Device-to-Device (D2D) pairs reuse the spectrum resources allocated to one cellular user, and propose a novel algorithm for improving the signal-to-interference-plus-noise ratio (SINR) of D2D communication. We call this D2D SINR improvement algorithm as DSIA, and design it from the perspective of precoding and decoding. With the DSIA, each D2D pair involved in the network can obtain the corresponding precoding and decoding vectors which make the SINR of its receiver being maximized. In order to clearly describe the design procedure of the proposed algorithm, we first formulate an optimization problem with multiple variables to maximize the SINR of each D2D receiver, then simplify the objective function through mathematical derivation and seek out the relationship between the optimized variables which can transform the optimization problem to one of solving nonlinear equations, and finally propose the DSIA to work out the nonlinear equations. Numerical results show that the DSIA will enable D2D to achieve significant performance gains in terms of SINR and bit-error rate (BER) compared with the traditional spectrum orthogonal scheme.

On the region of feasibility of interference alignment for underwater acoustic communication

To enable underwater applications such as coastal and tactical surveillance, undersea explorations, and picture/video acquisition, there is a need to achieve high data-rate underwater acoustic communications, which translates into attaining high acoustic channel spectral efficiencies. Interference Alignment (IA) technique, which has recently been proposed for radio-frequency MIMO terrestrial communication systems, aims at improving the spectral efficiency by enabling nodes to transmit data simultaneously at a rate equal to half of the interference-free channel capacity. The core of IA lies in designing transmit precoding matrices for each transmitter such that all the interfering signals align at the receiver along a direction different from the desired signal. To decode, the receiver projects the received signal onto a decoding vector that is orthogonal to the data vector of interfering signal. While promising, there are challenges to be solved for the use of IA underwater, i.e., 1) imperfect acoustic channel knowledge, 2) high computational complexity, and 3) high communication delay. We study the feasibility of IA in underwater environment under these challenges; we also propose a distributed computational framework to parallelize the iterative IA algorithm and determine to what extent we can parallelize it among neighboring nodes under different channel coherence times.

Turbo Processing for Joint Channel Estimation, Synchronization, and Decoding in Coded MIMO-OFDM Systems

This paper proposes a turbo joint channel estimation, synchronization, and decoding scheme for coded multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The effects of carrier frequency offset (CFO), sampling frequency offset (SFO), and channel impulse responses (CIRs) on the received samples are analyzed and explored to develop the turbo decoding process and vector recursive least squares (RLSs) algorithm for joint CIR, CFO, and SFO tracking. For burst transmission, with initial estimates derived from the preamble, the proposed scheme can operate without the need of pilot tones during the data segment. Simulation results show that the proposed turbo joint channel estimation, synchronization, and decoding scheme offers fast convergence and low mean squared error (MSE) performance over quasistatic Rayleigh multipath fading channels. The proposed scheme can be used in a coded MIMO-OFDM transceiver in the presence of multipath fading, carrier frequency offset, and sampling frequency offset to provide a bit error rate (BER) performance comparable to that in an ideal case of perfect synchronization and channel estimation over a wide range of SFO values.

Adaptive Hybrid Genetic Algorithm Parallel Interference Cancellation High Rate Multi-User Detection for Dual Rate W-CDMA Mobile Communications

Adaptive hybrid genetic algorithm concatenated with improved parallel interference cancellation, i.e. adaptive hybrid genetic algorithm parallel interference cancellation (AHGAPIC) was proposed. A study is conducted on the application of AHGAPIC to soft decoding high rate multi-user detection with diversity reception for dual-rate wideband DS-CDMA spread spectrum communications, aiming to mitigate the effect of multiple access interference. The relevant research has revealed that the local search capability of hybrid genetic algorithm (HGA) is still not good enough. Therefore, first, two evolutionary operations, i.e. inversion and insertion are merged into HGA to constitute a novel algorithm. With its moderate local search capability, this new algorithm can search for the global optimum region according to the information entropy, and then it is made adaptively vary its probabilities of crossover and mutation depending on the fitness values of the solutions to form the adaptive hybrid genetic algorithm (AHGA). Second, AHGA is utilized to effectively identify the better and better binary string to maximize the log-likelihood function of dual-rate multi-user detection. As AHGA converges to the optimum region, the control factor of the improved parallel interference cancellation (IPIC) detector is set to be the ratio of the average fitness value to the maximum fitness value of the population of AHGA. Finally, equipped with both the control factor and the binary string with the maximum fitness value as the initial data, the IPIC detector can rapidly find out the approximately optimum soft decoding vector. Then, it can obtain the approximately global optimum estimate point on the basis of the soft decoding rule, corresponding to the transmitted data bits. A lower bound of computational complexity has been achieved through simulations and qualitative analyses. The property of the proposed algorithm to converge rapidly leads to lower computational complexity. Emulation results have shown that the AHGAPIC soft decoding high rate multi-user detector is superior to other suboptimum detectors considered in this paper in terms of two points. They are the mitigation of multiple access interference and the resistance to near-far effects. Its performance is close to the sequential group optimum multi-user detector but with a shorter time delay.