Showing papers on "Dirty paper coding published in 2022"

Maximizing dirty-paper coding rate of RIS-assisted multi-user MIMO broadcast channels

Abstract: We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces (RISs) to maximize the dirty-paper-coding (DPC) sum rate of the RIS-assisted broadcast channel. Different from prior works, which maximize the rate achievable by linear precoders, we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast channel. We propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold optimization. Our results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.

A Practical Dirty Paper Coding Scheme for MISO Broadcast Channels

Abstract: In this work, we analyze a probabilistic shaping based dirty paper coding (DPC) scheme on a two user multiple input single output (MISO) broadcast channel (BC) and evaluate rate region of the users in the finite block length regime by utilizing analytic approximations introduced for shaping and channel coding parts of DPC. The DPC scheme can be implemented with low complexity and enlarges rate region of the users in finite block length regime substantially compared to linear precoding and time sharing strategies.

On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems

Abstract: The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users' target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed. Numerical results are presented to reveal the impact factors of QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems

Abstract: The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users' target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed. Numerical results are presented to reveal the impact factors of QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

Deterministic $K$-identification For Slow Fading Channel

Abstract: Deterministic $K$-identification (DKI) is addressed for Gaussian channels with slow fading (GSF), where the transmitter is restricted to an average power constraint and channel side information is available at the decoder. We derive lower and upper bounds on the DKI capacity when the number of identifiable messages $K$ may grow sub-linearly with the codeword length $n$. As a key finding, we establish that for deterministic encoding, assuming that the number of identifiable messages $K = 2^{\kappa \log n}$ with $\kappa \in [0,1)$ being the identification target rate, the codebook size scales as $2^{(n\log n)R}$, where $R$ is the coding rate.

A Finite Blocklength Coding Scheme for the Dirty-Tape Channel with Noisy Feedback

Abstract: In this paper, the dirty-tape channel (the white Gaussian channel with causal state interference at the trans-mitter) is revisited by considering a noisy feedback link between the transmitter and receiver. A finite blocklength (FBL) coding scheme is proposed for this model, which is based on the classical Schalkwijk-Kailath (SK) scheme and an existing lattice-based interference canceling scheme. Based on the proposed scheme, an achievable rate for given blocklength and decoding error probability is obtained, and the results of this paper are further illustrated via numerical examples.

A Simple Approach to Practical Dirty Paper Coding

Abstract: This paper presents a simple dirty paper coding (DPC) scheme based on q-ary codes over integer rings. The encoding primarily involves a linear subtraction in the integer ring, for pre-cancellation of interference known at the transmitter. The received signal sequence corrupted by the interference is then guaranteed to belong to the expanded codebook of the ring code. As such, iterative q-ary message passing algorithm over the expanded codebook is employed for decoding of linear DPC, yielding near-capacity performance at the medium-to-high SNR regime for bandwidth efficient communication. The complexity of the proposed linear DPC is almost identical to that of the conventional coding for AWGN channel, and the power penalty is shown to quickly vanish as q increases. We employ a generalize EXIT chart based technique to optimize the degree distributions of the components and the multipliers of the underlying doubly irregular repeat-accumulate ring codes. Numerical results demonstrate that the gaps to capacity limit of the interference-free AWGN channel are only 1.36, 0.91, 0.62 dB for spectral efficiency of 2.0, 2.5, 3.0 bits/symbol, respectively.

(Non)-Coherent MU-MIMO Block Fading Channels with Finite Blocklength and Linear Processing

Abstract: This paper studies the coherent and non-coherent multiuser multiple-input multiple-output (MU-MIMO) uplink system in the finite blocklength regime. The i.i.d. Gaussian codebook is assumed for each user. To be more specific, the BS first uses two popular linear processing schemes to combine the signals transmitted from all users, namely, MRC and ZF. Following it, the matched maximum-likelihood (ML) and mismatched nearest-neighbour (NN) decoding metric for the coherent and non-coherent cases are respectively employed at the BS. Under these conditions, the refined third-order achievable coding rate, expressed as a function of the blocklength, average error probability, and the third-order term of the information density (called as the channel perturbation), is derived. With this result in hand, a detailed performance analysis is then pursued, through which, we derive the asymptotic results of the channel perturbation, achievable coding rate, channel capacity, and the channel dispersion. These theoretical results enable us to obtain a number of interesting insights related to the impact of the finite blocklength: i) in our system setting, massive MIMO helps to reduce the channel perturbation of the achievable coding rate, which can even be discarded without affecting the performance with just a small-to-moderate number of BS antennas and number of blocks; ii) under the non-coherent case, even with massive MIMO, the channel estimation errors cannot be eliminated unless the transmit powers in both the channel estimation and data transmission phases for each user are made inversely proportional to the square root of the number of BS antennas; iii) in the non-coherent case and for fixed total blocklength, the scenarios with longer coherence intervals and smaller number of blocks offer higher achievable coding rate.

Capacity Achieving by Diagonal Permutation for MU-MIMO channels

Abstract: Dirty Paper Coding (DPC) is considered as the optimal precoding which achieves capacity for the Gaussian Multiple-Input Multiple-Output (MIMO) broadcast channel (BC). However, to find the optimal precoding order, it needs to repeat N! times for N users as there are N! possible precoding orders. This extremely high complexity limits its practical use in modern wireless networks. In this paper, we show the equivalence of DPC and the recently proposed Higher Order Mercer's Theorem (HOGMT) precoding[1][2] in 2-D (spatial) case, which provides an alternate implementation for DPC. Furthermore, we show that the proposed implementation method is linear over the permutation operator when permuting over multi-user channels. Therefore, we present a low complexity algorithm that optimizes the precoding order for DPC with beamforming, eliminating repeated computation of DPC for each precoding order. Simulations show that our method can achieve the same result as conventional DPC with about 20 dB lower complexity for N = 5 users.

Linear Coding for Gaussian Two-Way Channels

Abstract: We consider linear coding for Gaussian two-way channels (GTWCs), in which each user generates the transmit symbols by linearly encoding both its message and the past received symbols (i.e., the feedback information) from the other user. In Gaussian one-way channels (GOWCs), Butman has proposed a well-developed model for linear encoding that encapsulates feedback information into transmit signals. However, such a model for GTWCs has not been well studied since the coupling of the encoding processes at the users in GTWCs renders the encoding design non-trivial and challenging. In this paper, we aim to fill this gap in the literature by extending the existing signal models in GOWCs to GTWCs. With our developed signal model for GTWCs, we formulate an optimization problem to jointly design the encoding/decoding schemes for both the users, aiming to minimize the weighted sum of their transmit powers under signal-to-noise ratio constraints. First, we derive an optimal form of the linear decoding schemes under any arbitrary encoding schemes employed at the users. Further, we provide new insights on the encoding design for GTWCs. In particular, we show that it is optimal that one of the users (i) does not transmit the feedback information to the other user at the last channel use, and (ii) transmits its message only over the last channel use. With these solution behaviors, we further simplify the problem and solve it via an iterative two-way optimization scheme. We numerically demonstrate that our proposed scheme for GTWCs achieves a better performance in terms of the transmit power compared to the existing counterparts, such as the non-feedback scheme and one-way optimization scheme.

Deep Learning Based Beamforming for MISO Systems with Dirty-Paper Coding

Abstract: Beamforming technique can effectively improve the spectrum utilization of multi-antenna systems, while the dirty-paper coding (DPC) technique can reduce inter-user interference. In this letter, we aim to maximize the weighted sum-rate under power constraint in a multiple-input-single-output (MISO) system with the DPC. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, by utilizing the deep learning technique and the uplink-downlink duality, and carefully exploring the optimal solution structure, we devise a beamforming neural network (BFNNet), which includes a deep neural network module and a signal processing module. Besides, we use the modulus of the channel coefficients as the input of deep neural network, which reduces the input size. Simulation results show that a well-trained BFNNet can achieve near-optimal solutions, while significantly reducing computational complexity

Linear Coding for Gaussian Two-Way Channels

Abstract: We consider linear coding for Gaussian two-way channels (GTWCs), in which each user generates the transmit symbols by linearly encoding both its message and the past received symbols (i.e., the feedback information) from the other user. In Gaussian one-way channels (GOWCs), Butman has proposed a well-developed model for linear encoding that encapsulates feedback information into transmit signals. However, such a model for GTWCs has not been well studied since the coupling of the encoding processes at the users in GTWCs renders the encoding design non-trivial and challenging. In this paper, we aim to fill this gap in the literature by extending the existing signal models in GOWCs to GTWCs. With our developed signal model for GTWCs, we formulate an optimization problem to jointly design the encoding/decoding schemes for both the users, aiming to minimize the weighted sum of their transmit powers under signal-to-noise ratio constraints. First, we derive an optimal form of the linear decoding schemes under any arbitrary encoding schemes employed at the users. Further, we provide new insights on the encoding design for GTWCs. In particular, we show that it is optimal that one of the users (i) does not transmit the feedback information to the other user at the last channel use, and (ii) transmits its message only over the last channel use. With these solution behaviors, we further simplify the problem and solve it via an iterative two-way optimization scheme. We numerically demonstrate that our proposed scheme for GTWCs achieves a better performance in terms of the transmit power compared to the existing counterparts, such as the non-feedback scheme and one-way optimization scheme.

Full-Duplex Multi-User MIMO Communication Systems Performance Optimization Using Leakage-based Precoding

Abstract: Abstract The spectral efficiency (SE) can approximately double when using full-duplex multiuser MIMO communications. However, there are difficulties because of multiuser interferences, self-interference (SI), and co-channel interference (CCI). To improve the SE of the DL, this paper proposes CCI-aware enhancement to SLNR (signal-to-leakage-and-noise-ratio) and considers a suppressing filter at the receiver to cancel the interferences again designing a beamformer based on CCI-plus-noise covariance matrices for every user at the transmitting side. Additionally, we propose an improvement in the SLNR method by using SI-plus-noise covariance matrices to design UL beamformers. Unlike zero-forcing and block-diagonalization, the SLNR approach serves numerous antennas at users and BS (base station). The total SE of the communication yielded using the optimized precoder, i.e., obtained from the SLNR-based precoding. To achieve the maximum EE, we use a power consumption model. Simulation results confirm that full-duplex performs well compared to half-duplex when the number of antennas at every user in uplink as well downlink channels grow, for all Rician factors, for slight powers of the CCI and SI, and a limited number of antennas at the BS. With the proposed scheme for given transmit power and circuit power, we demonstrate that FD has a higher EE than HD.

Cellular and Cell‐Free Massive MIMO Techniques in 6G

Abstract: Multi-User Multiple-Input Multiple-Output (MU-MIMO) refers to the deployment scenario, where a base station equipped with multiple antennas serves multiple terminals with a single or only a few antennae. This chapter provides an information-theoretic introduction of MU-MIMO, that is, the sum capacity analyses of MIMO broadcast channels and MIMO multi-access channels. It discusses fundamentals of well-known dirty-paper coding that can achieve the full capacity, and the principles of its sub-optimal, low-complexity counterparts called zero-forcing precoding, and block diagonalization. The chapter examines the basic setup of massive MIMO, including the acquisition of channel knowledge, linear precoding in the downlink, and linear detection in the uplink. It then focuses on the layout of a cell-free massive MIMO network, Channel State Information acquisition via uplink training, and data transmission in the uplink.

A Practical Coding Scheme for the Two-Way Full-Duplex Dirty Paper Channel

Abstract: In this paper, the two-way full-duplex Gaussian channel (TW-FD-GC) is revisited by considering Gaussian state interference non-causally known by the corresponding transmitter, which is also called the two-way full-duplex dirty paper channel (TW-FD-DPC). We propose a novel coding scheme for the TW-FD-DPC, which perfectly eliminates the effect of the state interference. Numerical result shows that for a fixed decoding error probability, the encoding-decoding complexity of this scheme is extremely low.

On the Sum Capacity Achieving Precoders and Their CSI Requirements for MIMO-BC

Abstract: Dirty paper coding (DPC) precoders achieve the sum capacity of Gaussian MIMO broadcast channels. However, most systems employ zero-forcing (ZF) and MMSE precoders that are near-optimal at high SNR if the base station (BS) has a large number of antennas, but achievable sum rates with ZF and MMSE precoders are significantly below sum capacity otherwise. We show that capacity achieving DPC precoding requires CSI that scales only as $O(M^{2})$ for $M$ -antenna user (not full CSI as is generally assumed) irrespective of the number of BS antennas. Moreover, easily computable generalized decision feedback equalizers significantly outperform MMSE and ZF precoders.