Showing papers on "Dirty paper coding published in 2021"

Power Efficient IRS-Assisted NOMA

Abstract: In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.

Intelligent Reflecting Surface Enabled Random Rotations Scheme for the MISO Broadcast Channel

Abstract: The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which the reflecting elements only employ random phase rotations without requiring any CSI. The only CSI then needed is at the base station (BS) of the overall channel to implement the beamforming transmission scheme. Under this framework, we derive the sum-rate scaling laws in the large number of users regime for the IRS-assisted multiple-input single-output (MISO) broadcast channel, with optimal dirty paper coding (DPC) scheme and the lower-complexity random beamforming (RBF) and deterministic beamforming (DBF) schemes at the BS. The random rotations scheme increases the sum-rate by exploiting multi-user diversity, but also compromises the gain to some extent due to correlation. Finally, energy efficiency maximization problems in terms of the number of BS antennas, IRS elements and transmit power are solved using the derived scaling laws. Simulation results show the proposed scheme to improve the sum-rate, with performance becoming close to that under coherent beamforming for a large number of users.

On Linearly Precoded Rate Splitting for Gaussian MIMO Broadcast Channels

Abstract: In this paper, we consider a general $K$ -user Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). We assume that the channel state is deterministic and known to all the nodes. While the private-message capacity region is well known to be achievable with dirty paper coding (DPC), we are interested in the simpler linearly precoded transmission schemes. In particular, we focus on linear precoding schemes combined with rate-splitting (RS). First, we derive an achievable rate region with minimum mean square error (MMSE) precoding at the transmitter and joint decoding of the sub-messages at the receivers. Then, we study the achievable sum rate of this scheme and obtain two findings: 1) an analytically tractable upper bound on the sum rate that is shown numerically to be a close approximation, and 2) how to reduce the number of active streams – crucial to the overall complexity – while preserving the sum rate to within a constant loss. The latter results in two practical algorithms: a stream elimination algorithm and a stream ordering algorithm. Finally, we investigate the constant-gap optimality of linearly precoded RS with respect to the capacity. Our result reveals that, while the achievable rate of linear precoding alone can be arbitrarily far from the capacity, the introduction of RS can help achieve the capacity region to within a constant gap in the two-user case. Nevertheless, we prove that the RS scheme’s constant-gap optimality does not extend to the three-user case. Specifically, we show, through a pathological example, that the gap between the sum rate and the sum capacity can be unbounded.

Transmit Design for Joint MIMO Radar and Multiuser Communications with Transmit Covariance Constraint.

Abstract: In this paper, we consider the design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In addition to a power constraint, we require the covariance of the transmit waveform be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. Considering that the interference cannot be completely eliminated with this constraint, we introduce dirty paper coding (DPC) to further cancel the interference, and formulate the SINR balancing and sum rate maximization problem in the DPC regime. Although both of the two problems are non-convex, we show that they can be reformulated to convex optimizations via the Lagrange and downlink-uplink duality. In addition, we propose gradient projection based algorithms to solve the equivalent dual problem of SINR balancing, in both transmit beamforming and DPC regimes. The simulation results demonstrate significant performance improvement of DPC over transmit beamforming, and also indicate that the degrees of freedom for the communication transmitter is restricted by the rank of the covariance.

Successive Null-Space Precoder Design for Downlink MU-MIMO with Rate Splitting and Single-Stage SIC

Abstract: In this paper, we consider the precoder design for an under-loaded or critically loaded downlink multi-user multiple-input multiple-output (MU-MIMO) communication system. We propose novel precoding and decoding schemes which enhance system performance based on rate splitting at the transmitter and single-stage successive interference cancellation at the receivers. The proposed successive null-space (SNS) precoding scheme utilizes linear combinations of the null-space basis vectors of the successively augmented MIMO channel matrices of the users as precoding vectors to adjust the inter-user-interference experienced by the receivers. We formulate a non-convex weighted sum rate (WSR) optimization problem, and solve it via successive convex approximation to obtain a suboptimal solution for the precoding vectors and the associated power allocation. Our simulation results reveal that the proposed SNS precoders outperform block diagonalization based linear and rate splitting designs, and in many cases, have a relatively small gap to the maximum sum rate achieved by dirty paper coding.

Optimal Sum Rate-Fairness Tradeoff for MIMO Downlink Communications Employing Successive Zero Forcing Dirty Paper Coding

Abstract: New power allocation schemes taking both sum-rate and fairness into account for MIMO downlink communications employing successive zero-forcing dirty paper coding are presented in this letter. Specifically, using a revised $\ell _{1}$ -norm fairness measure that allows for a more comprehensive consideration when users have an unequal number of receive antennas, the optimal tradeoff between sum-rate and fairness for MIMO downlink communications with arbitrary channel statistics is completely characterized. A novel stochastic power allocation scheme capable of achieving this optimal tradeoff is also given. To put the optimal tradeoff into practical use, an explicit rule for selecting operating sum-rate from the tradeoff is then proposed. Simulation results show that the new scheme can yield higher sum-rate and better fairness at the same time.

Multi User MIMO System with Block Diagonalization Precoding

Abstract: In multi-user MIMO, the capacity achieving techniques are precoding strategies usually accomplish affordable overall performance with much lower intricacy. Linear precoding strategies include zero-forcing (ZF) precoding customized for low-rate feedback of channel state information (CSI). Non-linear precoding is designed essentially dependent on the concept of dirty paper coding (DPC), which indicates that any recognized interference on the transmitter might be subtracted without the chastisement of radio resources if a definitive precoding scheme can be carried out on the transmit signal. The block diagonalization (BD) is a linear precoding approach for multi-user multi-input multi-output (MU-MIMO) broadcast channels, that's capable of completely get rid of the multi-user interference (MUI), but it isn't computationally efficient. This paper proposes the block diagonalization approach in order to achieve the MIMO broadcast channel capacity. The intricacy analysis recommends that the proposed approach is more efficient than the other precoding techniques in terms of the quantity of required computations.

Bosonic Dirty Paper Coding

Abstract: The bosonic channel is addressed with modulation interference and side information at the transmitter. The model can be viewed as the quantum counterpart of the classical random-parameter Gaussian channel. Based on Costa's writing-on-dirty-paper result, the effect of the interference can be canceled. For both homodyne and heterodyne detection, we observe the same phenomenon, as the model reduces to a classical Gaussian channel. Then, we consider the bosonic channel with joint detection, for which the classical results do not apply, and derive a dirty-paper coding lower bound. We demonstrate that the optimal coefficient for dirty paper coding is not necessarily the MMSE estimator coefficient as in the classical setting.

Precoder Design and Power Allocation for Downlink MIMO-NOMA via Simultaneous Triangularization

Abstract: In this paper, we consider the downlink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. The proposed precoding scheme is based on simultaneous triangularization and decomposes the MIMO-NOMA channels of the two users into multiple single-input single-output NOMA channels, assuming low-complexity self-interference cancellation at the users. In contrast to the precoding schemes based on simultaneous diagonalization (SD), the proposed scheme avoids inverting the MIMO channels of the users, thereby enhancing the ergodic rate performance. Furthermore, we develop a power allocation algorithm based on the convex-concave procedure, and exploit it to obtain the ergodic achievable rate region of the proposed MIMO-NOMA scheme. Our results illustrate that the proposed scheme outperforms baseline precoding schemes based on SD and orthogonal multiple access for a wide range of user rates and performs close to the dirty paper coding upper bound. The ergodic rate region can further be improved by utilizing a hybrid scheme based on time sharing between the proposed MIMO-NOMA scheme and point-to-point MIMO.

Quasi-Degradation Probability of Two-User NOMA Over Rician Fading Channels

Abstract: Non-orthogonal multiple access (NOMA) has a great potential to offer a higher spectral efficiency of multi-user wireless networks than orthogonal multiples access (OMA). Previous work has established the condition, referred to quasi-degradation (QD) probability, under which NOMA has no performance loss compared to the capacity-achieving dirty paper coding for the two-user case. Existing results assume Rayleigh fading channels without line-of-sight (LOS). In many practical scenarios, the channel LOS component is critical to the link quality where the channel gain follows a Rician distribution instead of a Rayleigh distribution. In this work, we analyze the QD probability over multi-input and single-output (MISO) channels subject to Rician fading. The QD probability heavily depends on the angle between two user channels, which involves a matrix quadratic form in random vectors and a stochastic matrix. With the deterministic LOS component, the distribution of the matrix quadratic form is non-central that dramatically complicates the derivation of the QD probability. To remedy this difficulty, a series of approximations is proposed that yields a closed-form expression for the QD probability over MISO Rician channels. Numerical results are presented to assess the analysis accuracy and get insights into the optimality of NOMA over Rician fading channels.

Intelligent Reflecting Surface Enabled Random Rotations Scheme for the MISO Broadcast Channel

Abstract: The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which the reflecting elements only employ random phase rotations without requiring any CSI. The only CSI then needed is at the base station (BS) of the overall channel to implement the beamforming transmission scheme. Under this framework, we derive the sum-rate scaling laws in the large number of users regime for the IRS-assisted multiple-input single-output (MISO) broadcast channel, with optimal dirty paper coding (DPC) scheme and the lower-complexity random beamforming (RBF) and deterministic beamforming (DBF) schemes at the BS. The random rotations scheme increases the sum-rate by exploiting multi-user diversity, but also compromises the gain to some extent due to correlation. Finally, energy efficiency maximization problems in terms of the number of BS antennas, IRS elements and transmit power are solved using the derived scaling laws. Simulation results show the proposed scheme to improve the sum-rate, with performance becoming close to that under coherent beamforming for a large number of users.

An FPGA Implementation of the Log-MAP Algorithm for a Dirty Paper Coding CODEC

Abstract: This work describes the log-MAP (BCJR) algorithm implementation of a close to capacity dirty paper coding CODEC. The CODEC consists of eight deep pipeline processors. It decodes blocks of 975 bits in 26.9 ms using less than 9.7% of low-cost FPGA (and no DSP blocks). Two pipelines, for alpha and beta, calculate the values of gamma (of the BCJR) to reduce the storage requirements. The final log-likelihood ratio (LLR) is calculated together with alpha, reusing intermediate results. The number of bits used by the different signals of the processor is easily configurable. It was set to six bits to the channel measure signals and eight bits to log of probability signals like alpha, beta, and others. The CODEC clock was 100 MHz. The achieved bit rate is 36.2 Kbps per CODEC, but multiple CODECs can be fit into a single chip. The CODEC is 3.49 dB from the channel capacity.

A Practical Low Delay Digital Scheme for Wyner-Ziv Coding over Gaussian Broadcast Channel

Abstract: In this paper, we propose a practical low delay digital scheme for lossy transmission of a Gaussian source over Gaussian broadcast channel when there is correlated side information at the receivers. Focusing on two receivers scenario, a bit-filling criterion is applied at the transmitter, to establish a connection between the common layer and refinement layer data. Then, we complete the dirty paper coding according to jointly typicality and finally superimpose the two layers for transmission. At the receiver, we propose a modified log-likelihood ratio-belief propagation decoding algorithm to utilize side information. And a typical-set based decoding algorithm is introduced to be an effective supplement if the decoding decision of the successive canceling method fails. Simulated results show that the proposed typical set decoding algorithm plays an excellent part in refinement layer recovery, and on the whole, the proposed digital scheme renders better reconstruction accuracy than the corresponding low delay separate source channel coding.

Cognitive Overlay for Inter-system Cellular Dynamic Spectrum Sharing in the Downlink

Abstract: This paper assesses the feasibility of a novel dynamic spectrum sharing approach for a cellular downlink based on cognitive overlay to allow non-orthogonal cellular transmissions from a primary and a secondary radio access technology concurrently on the same radio resources. The 2-user Gaussian cognitive interference channel is used to model a downlink scenario in which the primary and secondary base stations are co-located. A system architecture is defined that addresses practical challenges associated with cognitive overlay, in particular the non-causal knowledge of the primary user message at the cognitive transmitter. A cognitive overlay scheme is applied that combines superposition coding with dirty paper coding, and a primary user protection criterion is derived that is specific to a scenario in which the primary system is 4G while the secondary system is 5G. Simulation is used to evaluate the achievable signal-to-interference-plus-noise ratio (SINR) at the 4G and 5G receivers, as well as the cognitive power allocation parameter as a function of distance. Results suggest that the cognitive overlay scheme is feasible when the distance to the 5G receiver is relatively small, even when a large majority of the secondary user transmit power is allocated to protecting the primary user transmission. Achievable link distances for the 5G receiver are on the order of hundreds of meters for an urban macrocell or a few kilometers for a rural macrocell.

Partial Perturbation to Alleviate the Performance Degradation of Vector Perturbation With Inaccurate Power Scaling Factors

Abstract: For multi-user multiple-input-multiple-output (MU-MIMO) system, transmitter utilizes a pre-equalizer based precoding to cancel inter-stream interference for parallel transmission with the aid of accurate feedback of channel state information (CSI) from all receivers. The correct power scaling factor, which normalizes the symbols power of precoding to be a constant one, is required at each receiver. Due to CSI error and limitation of feed-forward link, the received inaccurate power scaling factor will shrink or expand the received constellation points which severely degrades the performance of MIMO system. In this paper, using nonlinear vector perturbation (VP) and linear zero-forcing (ZF) precoding, we analyze the impact of inaccurate power scaling factor on the performance of a MIMO precoding system. The analyzed results show that the mismatched modulo size from inaccurate power scaling factor severely degrades the performance of the VP system, especially for high order M-QAM modulation. In addition, the performance degradation is strongly related with the distribution of representation points for each M-QAM symbol. For linear ZF precoding, the performance loss from the shrinked or expanded constellation points is severe for system using high order M-QAM. In addition, to alleviate the performance degradation, we propose a VP system using partial perturbation points (PPP). By limiting the region of redundant points with vector perturbation for partial M-QAM symbols, the performance degradation due to the inaccurate power scaling factor can be alleviated.

Sum Capacity Loss Quantification With Optimal and Sub-Optimal Precoding in Heterogeneous Multiuser Channels

Abstract: We analytically approximate the expected sum capacity loss between the optimal downlink precoding technique of dirty paper coding (DPC), and the sub-optimal technique of zero-forcing precoding, for multiuser channels. We also consider the most general case of multi-stream transmission to multiple users, where we evaluate the expected sum capacity loss between DPC and block diagonalization precoding. Unlike previously, assuming heterogeneous Ricean fading, we utilize the well known affine approximation to predict the expected sum capacity difference between both precoder types (optimal and sub-optimal) over a wide range of system and propagation parameters. Furthermore, for single-stream transmission, we consider the problem of weighted sum capacity maximization, where a similar quantification of the sum capacity difference between the two precoder types is presented. In doing so, we disclose that power allocation to different users proportional to their individual weights asymptotically maximizes the weighted sum capacity. Numerical simulations are presented to demonstrate the tightness of the developed expressions relative to their simulated counterparts.

Bosonic Dirty Paper Coding

Abstract: The single-mode bosonic channel is addressed with classical interference in the modulation and with side information at the transmitter. This model can viewed as the quantum counterpart of the classical random-parameter Gaussian channel. Based on Costa's writing-on-dirty-paper result (1983), the effect of the channel parameter can be canceled even when the decoder has no side information, and regardless of the input power constraint. For both homodyne and heterodyne detection with a coherent-state protocol, the model reduces to a classical channel with either real or complex-valued Gaussian noise. Thereby, by applying Costa's dirty paper coding strategy, we observe that the effect of the classical interference can be canceled for those channels as well. Then, we consider the bosonic channel with joint detection, for which the classical results do not apply, and derive a dirty-paper coding lower bound. Furthermore, considering the special case of a pure-loss bosonic channel, we demonstrate that the optimal coefficient for dirty paper coding is not necessarily the MMSE estimator coefficient as in the classical setting.

Linear Precoding Techniques for MIMO Systems

Abstract: Precoding is a method used in Multiple Input and Multiple Output t antennas (MIMO) technology to achieve parallel data at the receiver with reduced Bit Error Rate (BER) and with increased channel capacity. There two different precoding techniques available, namely Linear Precoding Technique (LPT) and Non-linear Precoding Techniques (N-LPT). LPT include different methods like Singular Value Decomposition (SVD), Block Diagonalization (BD) and N_LPT like Dirty Paper Coding (DPC). N-LPT have high computational complexity. Thus analysis is done for the existing LPT’s which make use of Channel State Information (CSI) both at transmitter and receiver end to reduce computational complexity. In this paper, SVD and BD linear precoding techniques are simulated and compared their BER for different the modulation methods similar to BPSK, QPSK and 8-PSK using MATLAB for 4 × 4 MIMO. Two different equalization specifically Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) are considered for BER calculation. BER is reduced by 5% with SVD precoding using QPSK modulation than without precoding. Comparing BER of SVD and BD techniques for a 4 × 4 MIMO using QPSK modulation, 90% better results have been achieved in BD. A 2 × 2 MIMO with SVD precoding system was implemented using Universal Software Radio Peripheral (USRP-2920) and the results are plotted.

Successive Null-Space Precoder Design for Downlink MU-MIMO with Rate Splitting and Single-Stage SIC

Abstract: In this paper, we consider the precoder design for an under-loaded or critically loaded downlink multi-user multiple-input multiple-output (MU-MIMO) communication system. We propose novel precoding and decoding schemes which enhance system performance based on rate splitting at the transmitter and single-stage successive interference cancellation at the receivers. The proposed successive null-space (SNS) precoding scheme utilizes linear combinations of the null-space basis vectors of the successively augmented MIMO channel matrices of the users as precoding vectors to adjust the inter-user-interference experienced by the receivers. We formulate a non-convex weighted sum rate (WSR) optimization problem, and solve it via successive convex approximation to obtain a suboptimal solution for the precoding vectors and the associated power allocation. Our simulation results reveal that the proposed SNS precoders outperform block diagonalization based linear and rate splitting designs, and in many cases, have a relatively small gap to the maximum sum rate achieved by dirty paper coding.