Low-Overhead dynamic codebook design for RIS-Aided multiuser MISO systems

Multiple transmit antennas can be employed in a downlink channel to serve multiple users under the assumption of perfect channel state information (CSI) of all users at the base station. In practice, however, the channel knowledge available at the base station is not perfect. For instance, in case of a time division duplex (TDD) system, the transmit CSI is the estimated uplink channel. On the other hand, in a frequency division duplex (FDD) system, feedback from the users is required to obtain transmit CSI. In this case, we assume the transmit CSI consists of a quantized version of the normalized estimated channel vector and the statistics of the channel norm of each user. In this work we consider the transceiver design of the downlink of a multiuser MISO system based on the minimum mean square error (MMSE) criterion with imperfect CSI as discussed above.

This paper considers an optimal artificial noise (AN)-aided transmit design for multi-user MISO systems in the eyes of service integration. Specifically, two sorts of services are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver. The confidential message is kept perfectly secure from all the unauthorized receivers. This paper considers a general case of imperfect channel state information (CSI), aiming at a joint and robust design of the input covariances for the multicast message, confidential message and AN, such that the worst-case secrecy rate region is maximized subject to the sum power constraint. To this end, we reveal its hidden convexity and transform the original worst-case robust secrecy rate maximization (SRM) problem into a sequence of semidefinite programming. Numerical results are presented to show the efficacy of our proposed method.

Hybrid beamforming (HBF) is a promising approach to be employed in millimeter-wave massive MIMO systems. In this paper, four HBF algorithms with partially connected radio frequency architecture are proposed for large antenna arrays in multi-user MISO systems. The first two algorithms aim at minimizing the difference between either the fully digital zero forcing (ZF) or maximum ratio transmission (MRT) beamformer, and the hybrid beamformer of each user. The other two algorithms apply either ZF or MRT HBF solution to each subarray. The average sum rate performance of the proposed algorithms are evaluated using a realistic geometry-based stochastic channel model, and compared with digital ZF and MRT approaches. Numerical results demonstrate that the subarray-based ZF algorithm is superior to other proposed hybrid methods in all simulation cases.

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

Grassmannian quantization codebooks play a central role in a number of limited feedback schemes for single and multi-user MIMO communication systems. In practice, it is often desirable that these codebooks possess additional properties that facilitate their implementation, beyond the provision of good quantization performance. Although some good codebooks exist, their design tends to be a rather intricate task. The goal of this paper is to suggest a flexible approach to the design of Grassmannian codebooks based on smooth optimization algorithms for the Grassmannian manifold and the use of smooth penalty functions to obtain additional desirable properties. As one example, rank-2 codebooks with a nested structure and elements from a discrete alphabet are designed. In some numerical comparisons, codebooks designed using the proposed approach have better Fubini-Study distance properties than some existing codebooks, and provide tangible performance gains when applied to a simple MIMO downlink scenario with zero-forcing beamforming, PU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> RC, and block diagonalization signalling. Furthermore, the proposed approach yields codebooks that attain desirable additional properties without incurring a substantial degradation in performance.

In bottom-up cross-layer optimization, each layer offers a set of feasible operation points to the layer above, enabling the application to decide which parameter setup is optimum. In this paper, the capabilities of the physical layer in a multi-user MISO system are described in terms of the users' outage probabilities. Optimum descriptions are given by sets of efficient outage probability tuples. We first derive a closed-form expression for a user's outage probability in the presence of multi-user interference. Next, the set of efficient outage probability tuples under a simplified SDMA transmit strategy is derived. We show how a good approximate description of the efficient set is found by equidistant sampling. Finally, the performance of the proposed transmit strategy is compared with an orthogonal multiple access scheme, demonstrating that optimum mode selection at the physical layer can lead to significant performance gains at the application layer

Owing to the unique advantages of low cost and controllability, reconfigurable intelligent surface (RIS) is a promising candidate to address the blockage issue in millimeter wave (mmWave) communication systems, consequently has captured widespread attention in recent years. However, the joint active beamforming and passive beamforming design is an arduous task due to the high computational complexity and the dynamic changes of wireless environment. In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm to jointly design active hybrid beamformer at the base station (BS) side and passive beamformer at the RIS side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a maximum entropy based DRL algorithm, which can explore more stochastic policies than deterministic policy, to design active analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method. The experimental results demonstrate that our proposed SAC algorithm can achieve better performance compared with conventional optimization algorithm and DRL algorithm.

Custom arithmetic is a novel and successful technique to reduce the computation and resource utilization of ASR systems running on mobile devices. It represents all floating-point numbers by integer indices and substitutes a sequence of table lookups for all arithmetic operations. The first and crucial step in custom arithmetic design is to quantize system variables, preferably to low precision. This paper explores several techniques to quantize variables with high entropy, including a reordering of Gaussian computation and a normalization of Viterbi search. Furthermore, a discriminatively inspired distortion measure is investigated for scalar quantization to better maintain recognition accuracy. Experiments on an isolated word recognition show that each system variable can be scalar quantized to less than 8 bits using a standard quantization method, except for the alpha probability in Viterbi search which requires 10 bits. However, using our normalization and discriminative distortion measure, the, forward probability can be quantized to 9 bits, thereby halving the corresponding lookup table size. This greatly reduces the memory bandwidth and enables the implementation of custom arithmetic on ASR systems.

Precoding codebook design for limited feedback MIMO systems is known to reduce to a discretization problem on a Grassmann manifold. The case of two-antenna beamforming is special in that it is equivalent to quantizing the real sphere. The isometry between the Grassmannian G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℂ</sup> and the real sphere <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> shows that discretization problems in the Grassmannian G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℂ</sup> are directly solved by corresponding spherical codes. Notably, the Grassmannian line packing problem in ℂ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , namely maximizing the minimum distance, is equivalent to the Tammes problem on the real sphere, so that optimum spherical packings give optimum Grassmannian packings. Moreover, a simple isomorphism between G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℂ</sup> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> enables to analytically derive simple codebooks in closed-form having low implementation complexity. Using the simple geometry of some of these codebooks, we derive closed-form expressions of the probability density function of the relative SNR loss due to limited feedback. We also investigate codebooks based on other spherical arrangements, such as solutions maximizing the harmonic mean of the mutual distances among the codewords, which is known as the Thomson problem. We find that in some special cases, Grassmannian codebooks based on these other spherical arrangements outperform codebooks from Grassmannian packing.

Communications system with analog or hybrid analog/digital architectures usually relies on a pre-defined codebook to perform beamforming. With the increase in the size of the antenna array, the characteristics of the spherical wavefront in the near-field situation are not negligible. Therefore, it is necessary to design a codebook that is adaptive to near-field scenarios. In this letter, we investigate the hierarchical codebook design method in the near-field situation. We develop a steering beam gain approximation method and design the lower-layer codebook to provide coverage for the Fresnel region. For the upper-layer codebook, we propose beam rotation and beam relocation methods to place an arbitrary beam pattern to a target location. The simulation results show the superiority of the proposed near-field hierarchical codebook design.

In frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems, the codebook-based beamforming has been widely considered to overcome the overhead of channel state information (CSI) feedback while there is still inevitable complexity. In order to further reduce that or help with the codebook scanning, it is valuable to exploit partial reciprocity to reduce the complexity. In this letter, we propose an angle-adaptive subspace codebook for FDD systems with massive MIMO. By leveraging the partial reciprocity of FDD systems, it is possible to estimate the angle information of the downlink channel based on the angle information of the uplink channel. This estimation enables the construction of a subspace codebook to efficiently help with the beamforming process. The size of the proposed codebook is proportional to the number of primary arrival paths, which is much smaller than the number of antennas in massive MIMO systems. Simulation results show that the proposed angle-adaptive subspace codebook can significantly reduce the complexity of codebook scanning while maintaining satisfactory performance compared to the simple codebook.

Reconfigurable intelligent surfaces (RISs) play a vital role in future wireless systems with the capability of enhancing propagation environments by intelligently reflecting the signals toward the target receivers. However, optimal tuning of the phase shifters at the RIS is challenging due to the passive nature of reflective elements and the high complexity of acquiring channel state information (CSI). Furthermore, the joint active beamforming and RIS reflection beam design is a tedious task due to the high computational complexity and the dynamic nature of the wireless environment. Today’s cellular networks establish data transmission by relying on pre-defined generic beamforming codebooks, which are neither site-specific nor adaptive to the changes in the wireless environment. Moreover, identifying the best beam is typically performed using an exhaustive search approach that prohibits the use of large codebook sizes due to the resulting high beam training overhead. Depending merely on the binary received signal strength, this work develops a multi-agent deep reinforcement learning (MA-DRL) framework that jointly designs the active and the passive reflection beam codebooks for the BS and the RIS, reflectively. To accelerate learning convergence and reduce the search space, the proposed model divides the RIS into multiple partitions and associates beam patterns to the surrounding environments with low computational complexity. Moreover, a hierarchical beam training solution is proposed to further reduce the beam training overhead of the single-beam training approach. Simulation results show that the proposed MA-DRL approach can provide a 97% beam training overhead reduction over the discrete Fourier transform (DFT) codebook.

Purpose The role of image compression is inevitable in telemedicine for the transfer of medical images. The diagnostic quality of the reconstructed image plays a vital role, and hence, the choice of compression algorithm is necessary. The medical images are analysed by the physicians for the diagnosis of anomalies. Procedures Linde Buzo Gray (LBG) is a classical algorithm for vector quantization; it produces a local optimum codebook that results in low compression efficiency. The efficiency of the vector quantization (VQ) relies on the appropriate codebook; hence, many research works based on optimization have developed for the generation of global codebook. This paper couples LBG with BAT optimization algorithm which generates an appropriate codebook. The optimization algorithm is employed not only for codebook design but also for the codebook size selection. Results The proposed BAT-LBG with dynamic codebook selection was compared with the classical LBG-VQ, JPEG lossless and BAT-LBG with static codebook selection. The algorithms were tested on abdomen CT images of five datasets, and performance evaluation was done by performance metrics like peak signal-to-noise ratio (PSNR) and mean square error (MSE). Conclusions The BAT-LBG compression with dynamic codebook size selection was found to produce efficient results when compared with JPEG lossless, classical LBG-VQ and BAT-LBG with static codebook selection. The quality of the reconstructed image was found to be good for BAT-LBG with dynamic codebook selection, and hence, it is efficient for transfer of medical images in telemedicine.

Limited feedback techniques, that employ codebook-based quantisation, are used in coordinated multi-point (CoMP) transmission to convey the channel direction information between the mobile user and each base station in the cooperative set and also among the cooperating base stations. However, unlike single-cell multiple-user multiple input multiple output (MU-MIMO) systems, CoMP systems face additional challenges in this codebook design. Among these challenges are the huge feedback overhead and the need for dynamic codebook size. These problems are solved when CoMP uses the so-called per-cell codebook, in which the channel to each cell is quantised separately. However, this results in a further problem, that is having random phase values between the quantised per-cell channels of the same user. In this study, the phase ambiguity problem is explained and its effect on performance degradation is quantified. A new quantisation technique is proposed where the quantisation of each of the real and the imaginary parts is performed independently using the same real codebook. This novel solution prevents the phase ambiguity problem instead of trying to mitigate its effects, in addition to simplifying the codebook design. The proposed quantisation technique outperforms two recently introduced solutions in the literature. The first solution is to quantise phase ambiguity as part of channel state information (CSI), whereas the other avoids phase ambiguity by an iterative selection procedure. Mathematical validations and simulations are provided to verify these findings.

We consider a downlink multiuser MISO system with bounded errors in the Channel State Information at the Transmitter (CSIT). We first look at the robust design problem of achieving max-min fairness amongst users (in the worst-case sense). Contrary to the conventional approach adopted in literature, we propose a rather unorthodox design based on a Rate-Splitting (RS) strategy. Each user's message is split into two parts, a common part and a private part. All common parts are packed into one super common message encoded using a public codebook, while private parts are independently encoded. The resulting symbol streams are linearly precoded and simultaneously transmitted, and each receiver retrieves its intended message by decoding both the common stream and its corresponding private stream. For CSIT uncertainty regions that scale with SNR (e.g. by scaling the number of feedback bits), we prove that a RS-based design achieves higher max-min (symmetric) Degrees of Freedom (DoF) compared to conventional designs (NoRS). For the special case of non-scaling CSIT (e.g. fixed number of feedback bits), and contrary to NoRS, RS can achieve a non-saturating max-min rate. We propose a robust algorithm based on the cutting-set method coupled with the Weighted Minimum Mean Square Error (WMMSE) approach, and we demonstrate its performance gains over state-of-the art designs. Finally, we extend the RS strategy to address the Quality of Service (QoS) constrained power minimization problem, and we demonstrate significant gains over NoRS-based designs.