On precoding for constant K-User MIMO Gaussian interference channel with finite constellation inputs

We consider the wireless Rayleigh fading relay channel with finite complex input constellations. Assuming global knowledge of the channel state information and perfect synchronization, upper and lower bounds on the achievable rate, for the full-duplex relay, as well as the more practical half-duplex relay (in which the relay cannot transmit and receive simultaneously), are studied. Assuming the power constraint at the source node and the relay node to be equal, the gain in rate offered by the use of relay over the direct transmission (without the relay) is investigated. It is shown that for the case of finite complex input constellations, the relay gain attains the maximum at a particular SNR and at higher SNRs the relay gain tends to become zero. Since practical schemes always use finite complex input constellation, the above result means that the relay offers maximum advantage over the direct transmission when we operate at a particular SNR and offers no advantage at very high SNRs. This is contrary to the results already known for the relay channel with Gaussian input alphabet.

Consider a MIMO interference channel whereby each transmitter and receiver are equipped with multiple antennas. The basic problem is to design optimal linear transceivers (or beamformers) that can maximize system throughput. The recent work [1] suggests that optimal beamformers should maximize the total degrees of freedom and achieve interference alignment in high SNR. In this paper we first consider the interference alignment problem in spatial domain and prove that the problem of maximizing the total degrees of freedom for a given MIMO interference channel is NP-hard. Furthermore, we show that even checking the achievability of a given tuple of degrees of freedom for all receivers is NP-hard when each receiver is equipped with at least three antennas. Interestingly, the same problem becomes polynomial time solvable when each transmit/receive node is equipped with no more than two antennas. Finally, we propose a distributed algorithm for transmit covariance matrix design, while assuming each receiver uses a linear MMSE beamformer. The simulation results show that the proposed algorithm outperforms the existing interference alignment algorithms in terms of system throughput.

The Unshared Secret Key Cryptography (USK), recently proposed by the authors, guarantees Shannon's ideal secrecy and perfect secrecy for MIMO wiretap channels, without requiring secret key exchange. However, the requirement of infinite constellation inputs limits its applicability to practical systems. In this paper, we propose a practical USK scheme using finite constellation inputs. The new scheme is based on a cooperative jamming technique, and is valid even if the eavesdropper has more antennas than the transmitter. We show that Shannon's ideal secrecy can be achieved with an arbitrarily small outage probability.

It is well known that independent and identically distributed Gaussian inputs, scaled appropriately based on the signal-to-noise ratio (SNR), achieve capacity on the additive white Gaussian noise (AWGN) channel at all values of SNR. In this correspondence, we consider the question of whether such good input distributions exist for frequency-nonselective Rayleigh-fading channels, assuming that neither the transmitter nor the receiver has a priori knowledge of the fading coefficients. In this noncoherent regime, for a Gauss-Markov model of the fading channel, we obtain explicit mutual information bounds for the Gaussian input distribution. The fact that Gaussian input generates bounded mutual information motivates the search for better choices of fixed input distributions for high-rate transmission over rapidly varying channels. Necessary and sufficient conditions are derived for characterizing such distributions for the worst case scenario of memoryless fading, using the criterion that the mutual information is unbounded as the SNR gets large. Examples of both discrete and continuous distributions that satisfy these conditions are given. A family of fixed input distributions with mutual information growth rate of O((loglogSNR)/sup 1-u/), u>0 are constructed. It is also proved that there does not exist a single fixed-input distribution that achieves the optimal mutual information growth rate of loglogSNR.

Interference alignment is a key technique for communication scenarios with multiple interfering links. In several such scenarios, interference alignment was used to characterize the degrees of freedom of the channel. However, these degree-of-freedom capacity approximations are often too weak to make accurate predictions about the behavior of channel capacity at finite signal-to-noise ratios (SNRs). The aim of this paper is to significantly strengthen these results by showing that interference alignment can be used to characterize capacity to within a constant gap. We focus on real, time-invariant, frequency-flat X-channels. The only known solutions achieving the degrees of freedom of this channel are either based on real interference alignment or on layer-selection schemes. Neither of these solutions seems sufficient for a constant-gap capacity approximation. In this paper, we propose a new communication scheme and show that it achieves the capacity of the Gaussian X-channel to within a constant gap. To aid in this process, we develop a novel deterministic channel model. This deterministic model depends on the 1/2 log (SNR) most-significant bits of the channel coefficients rather than only the single most-significant bit used in conventional deterministic models. The proposed deterministic model admits a wider range of achievable schemes that can be translated to the Gaussian channel. For this deterministic model, we find an approximately optimal communication scheme. We then translate this scheme for the deterministic channel to the original Gaussian X-channel and show that it achieves capacity to within a constant gap. This is the first constant-gap result for a general, fully-connected network requiring interference alignment.

This paper investigates the linear precoder design for multiple-input multiple-output (MIMO) K-user interference channels with finite alphabet inputs. We first obtain the general explicit expressions of the achievable rate of each user in MIMO interference channel systems. We study optimal transmission strategies in both high signal-to-noise ratio (SNR) and low SNR regions. We show that given finite alphabet inputs, a simple power allocation design can achieve optimal performance. In contrast, the well-known interference alignment technique for Gaussian input scenarios, only utilizes a partial interference-free signal space for transmission and leads to a constant performance loss when it is applied to finite-alphabet input scenarios. We determine this constant rate loss at high SNR. Moreover, we establish necessary conditions for the linear precoder design of the weighted sum-rate maximization. We also develop an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results show that for the practical digital modulated signals from discrete constellations, the proposed iterative algorithm achieves considerably higher sum-rate than the existing methods.

A fundamental problem arising in dense wireless networks is the high co-channel interference. Interference alignment (IA) was recently proposed as an effective way to combat interference in wireless networks. The concept of IA, though, is originated by the capacity study of interference channels and as such, its performance is mainly gauged under ideal assumptions, such as instantaneous and perfect channel state information (CSI) at all nodes, and homogeneous signal-to-noise ratio (SNR) users, i.e., each user has the same average SNR. Consequently, the performance of IA under realistic conditions has not been completely investigated yet. In this paper, we aim at filling this gap by providing a performance assessment of spatial IA in practical systems. Specifically, we derive a closed-form expression for the IA average sum-rate when CSI is acquired through training and users have heterogeneous SNR. A main insight from our analysis is that IA can indeed provide significant spectral efficiency gains over traditional approaches in a wide range of dense network scenarios. To demonstrate this, we consider the examples of linear, grid and random network topologies.

This paper has investigated a downlink non-orthogonal multiple access (NOMA) cooperative scheme with user-assisted relaying, where near users are viewed as decode-and-forward (DF) relays operating full/half-duplex mode to assist multiple far users. Especially, the impact for the randomness of users location on the system performance has also been studied. Further, to overcome the zero diversity gain inherent to full-duplex (FD) operation, the direct link between a base station (BS) and each far user is exploited. The comprehensive performance analysis is conducted through the closed-form expressions in terms of outage probability, the diversity gain, system throughput, and ergodic sum rate. With respect to outage behavior, throughput, and ergodic sum rate, FD-based NOMA outperforms half-duplex (HD) NOMA in the lower signal-to-noise ratio (SNR) region. However, with the negative influence brought by residual loop-interference (LI), the advantage of FD NOMA scheme is not pronounced enough in the high SNR regime. Therefore, according to different SNR levels, we further propose a hybrid relaying scheme that switches opportunistically between the FD and HD modes. Afterward, the mode switching threshold is characterized such that the outage probability of each user is minimized. The simulation results confirm the correctness of our analysis and offer a significant guideline for system design in practice.

We consider the application of interference alignment (IA) to the cellular case with constant MIMO channel coefficients. Neglecting the weakest interferers in the network, we introduce an algorithm to obtain a partially connected (in particular, L-interfering) network, over which the feasibility of IA can be analyzed simply. We consider the application of IA over the obtained L-interfering network, while the rest of the interference is treated as noise. We benchmark this technique through extensive Monte-Carlo simulations, and introduce an approximate, compact expression for the ergodic mutual information achievable in the considered network setting.

NOMA-based cognitive radio network with hybrid FD/HD relay in industry 5.0

Interference alignment (IA) is an effective method that can eliminate interferences in wireless networks, and has been applied to spectrum sharing in cognitive radio (CR) networks recently. However, the availability of perfect network channel state information (CSI) is necessary for most existing IA schemes, which is not practical in general due to the realistic communication scenarios and deployment challenges. In this paper, we apply IA to cognitive relay networks under CSI mismatch where the variance of the CSI measurement error depends on signal-to-noise ratio (SNR). An adaptive Max-SINR IA algorithm has been introduced to improve the performance of the secondary network by using the knowledge of CSI error variance. Finally, we analyze the performance of the secondary network in terms of the end-to-end equivalent transmission rate and outage probability. Simulation results indicate that our proposed adaptive Max-SINR IA scheme can greatly improve the performance of the secondary network.

We consider distributed estimation in wireless sensor networks (WSNs). Using linear minimum mean squared error (LMMSE) estimator, we derive mean squared error (MSE) to measure the quality of estimation. We introduce a global signal to noise ratio (SNR), where we can derive capacity of data collection in terms of mutual information as reciprocity of MSE. Based on the global SNR, we derive equal power allocation and optimal power allocation in orthogonal multiple access channel (MAC) models. We also derive asymptotic behavior of the global SNR when the power and the number of sensors become unlimited. We minimize MSE as well as maximize mutual information by considering total and individual power constraints. We show that MSE and mutual information of equal power allocation outperforms optimal power allocation. Moreover, system with individual power constraint is worse than the system without that because the suggested power to be allocated is constrained by maximum transmit power of the sensors.

The two-way relaying scheme is a network coding scheme which provides efficient utilization of spectrum. The performance of a two-way relaying scheme based on analog network coding (ANC) can be evaluated in context of many parameters such as overall symbol error rate (OSER), overall outage probability (OOP) and ergodic sum rate (ESR). In this paper we analyse the OSER over Nakagami-m fading channels for analog network coding scheme. The variation of OSER with respect to signal to noise ratio (SNR) has been observed for various factors such as fading parameter, modulation scheme used and number of antennas at the source and destination terminal. The threshold range of SNR for a tolerable value of OSER of 0.001 is computed as 20 to 25 dB. The results demonstrate the necessity of high SNR in two way relaying scheme for tolerable OSER. Further the analysis may be useful for selection of various physical layer parameters in design of communication networks.

: Calculating the amount of information about a random function contained in another random function has important uses in communication theory. An expression for the mutual information for continuous time random processes has been given by Gelfand and Yaglom, Chiang, and Perez by generalizing Shannon's result in a natural way. Under a condition of absolute continuity of measures the continuous time expression has the same form as Shannon's result. For two Gaussian processes Gelfand and Yaglom express the mutual information in terms of a mean square estimation error. We generalize this result to diffusion processes and express the solution in a different form which is more naturally related to a corresponding filtering problem. We also use these results to calculate some information rates.

Rate-Splitting Multiple Access (RSMA) has recently appeared as a powerful and robust multiple access and interference management strategy for downlink Multi-user (MU) multi-antenna communications. In this work, we study the precoder design problem for RSMA scheme in downlink MU systems with both perfect and imperfect Channel State Information at the Transmitter (CSIT) and assess the role and benefits of transmitting multiple common streams. Unlike existing works which have considered single-antenna receivers (Multiple-Input Single-Output–MISO), we propose and extend the RSMA framework for multi-antenna receivers (Multiple-Input Multiple-Output–MIMO) and formulate the precoder optimization problem with the aim of maximizing the Weighted Ergodic Sum-Rate (WESR). Precoder optimization is solved using Sample Average Approximation (SAA) together with the proposed vectorization and Weighted Minimum Mean Square Error (WMMSE) based approach. Achievable sum-Degree of Freedom (DoF) of RSMA is derived for the proposed framework as an increasing function of the number of transmitted common and private streams, which is further validated by the Ergodic Sum Rate (ESR) performance using Monte Carlo simulations. Conventional MU–MIMO based on linear precoders and Non-Orthogonal Multiple Access (NOMA) schemes are considered as baselines. Numerical results show that with imperfect CSIT, the sum-DoF and ESR performance of RSMA is superior to those of the two baselines, and is increasing with the number of transmitted common streams. Moreover, by better managing the interference, RSMA not only has significant ESR gains over baseline schemes but is more robust to CSIT inaccuracies, network loads and user deployments.