We consider a cell-free massive multiple-input multiple-output (MIMO) system with multiple antennas on the users and access points (APs). In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pilots. This approach works well for single-antenna users. In this paper, we show that much higher SEs can be achieved if downlink pilots are sent when having multi-antenna users. The reason is that the effective channel matrix does not harden. We propose a pilot-based downlink estimation scheme, derive a new SE expression, and show numerically that it yields substantially higher performance when having correlated Rayleigh fading channels. In cases with multi-antenna users, the APs can either coherently transmit the same data streams, or alternatively they transmit separate data streams non-coherently. The latter approach reduces the fronthaul signaling overhead, albeit with a potential SE penalty. For both strategies, we propose novel precoding and combining schemes. Specifically, we develop precoders based on the minimum mean square error (MMSE) criterion and investigate receive combining methods, including an MMSE combiner. Furthermore, we consider sharing different levels of channel knowledge between the APs. Finally, we present a comprehensive numerical analysis to validate our findings, evaluating the performance trade-offs associated with the number of users, APs, and antennas, as well as the choice of transmission strategy and the level of CSI sharing among APs.

This work investigates a collaborative sensing and data collection system in which multiple uncrewed aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for each UAV and a common sensing task that is executed by all UAVs to enable cooperative transmission. Unlike existing studies, we explore the use of an advanced cell-free multiple-input-multiple-output (MIMO) network, which effectively manages inter-UAV interference. To further optimize wireless channel utilization, we propose a hybrid transmission strategy that combines time-division multiple access (TDMA), nonorthogonal multiple access (NOMA), and cooperative transmission. The problem of jointly optimizing task splitting ratios and the hybrid TDMA-NOMA-cooperative transmission strategy is formulated with the objective of minimizing mission completion time. Extensive numerical results demonstrate the effectiveness of the proposed task allocation and hybrid transmission scheme in accelerating the completion of sensing missions.

We consider a cell-free massive MIMO system with multiple antennas on the users and access points. In previous works, the downlink spectral efficiency (SE) has been evaluated using the hardening bound that requires no downlink pilots. This approach works well when having single-antenna users. In this paper, we show that much higher SEs can be achieved if downlink pilots are sent since the effective channel matrix does not harden when having multi-antenna users. We propose a pilot-based downlink estimation scheme and derive a new SE expression that utilizes zero-forcing combining. We show numerically how the number of users and user antennas affects the SE.

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weighted bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.

People with visual impairments (PVI) access photos through image descriptions. Thus far, research has studied what PVI expect in these descriptions mostly regarding functional purposes (e.g., identifying an object) and when engaging with online, publicly available images. Extending this research, we interviewed 30 PVI to understand their expectations for image descriptions when viewing, taking, searching, and reminiscing with personal photos on their own devices. We show how their expectations varied across photo activities and often went well beyond identifying objects in photos. Based on our findings, we propose design opportunities for generating and providing image descriptions for personal photo use by PVI. The design opportunities for PVI also point to novel support for the sighted for using image descriptions to enrich their experience of photos.

Autoencoder (AE) techniques have been intensively studied for the optimization of wireless transceivers. However, fixed computational structures of existing AE models lack the flexibility to the lengths of message bits and codewords. This work proposes a versatile AE framework, termed by autoencoding graph neural network (AEGNN), where both encoder and decoder are realized by GNNs. The viability of the proposed AEGNN is demonstrated in various application scenarios.

This work studies the uplink of a multi-tenant cloud radio access network (C-RAN) system with spectrum pooling. In the system, each operator has a cloud processor (CP) connected to a set of proprietary radio units (RUs) through finite-capacity fronthaul links. The uplink spectrum is divided into private and shared subbands, and all the user equipments (UEs) of the participating operators can simultaneously transmit signals on the shared subband. To mitigate inter-operator interference on the shared subband, the CPs of the participating operators can exchange compressed uplink baseband signals on finite-capacity backhaul links. This work tackles the problem of jointly optimizing bandwidth allocation, transmit power control and fronthaul compression strategies. In the optimization, we impose that the inter-operator privacy loss be limited by a given threshold value. An iterative algorithm is proposed to find a suboptimal solution based on the matrix fractional programming approach. Numerical results validate the advantages of the proposed optimized spectrum pooling scheme.

Cloud radio access network (C-RAN) is an emerging architecture for achieving high data rate and massive connectivity in future wireless communication systems. To further improve the performance of C-RAN downlink, this work considers the advantages of cooperation among user equipments (UEs). Specifically, it is assumed that the UEs can exchange the downlink received baseband signals via device-to-device (D2D) communication links which are orthogonal to downlink channel. The problem of jointly optimizing the C-RAN downlink and inter-UeD2d communication strategies is tackled with the goal of maximizing the sum-rate of UEs. To address the formulated problem, an iterative algorithm is proposed which attains a suboptimal solution of the problem. Numerical results confirm that the inter-UE D2D cooperation is effective to improve the downlink performance of C-RAN systems.

This work proposes an energy-efficient design of transmit power control and relaying strategies for the uplink of a cloud radio access network (C-RAN) with wireless fronthaul link. In the system, a set of single-antenna user equipments (UEs) send independent messages to a multi-antenna baseband processing unit (BBU) through a set of parallel multi-antenna remote radio heads (RRHs). The radio access link from the UEs to the RRHs is assumed to be orthogonal to the fronthaul link from the RRHs to the BBU. Since the overall performance of the system may be limited by the battery constraints of the UEs, this work tackles the problem of maximizing the worst energy efficiency of the UEs subject to the transmit power constraints at the UEs and the RRHs. Numerical results are provided to validate the advantages of the proposed energy-efficient scheme.

This work studies distributed uplink reception for a device-to-device (D2D) underlaid cloud radio access network (C-RAN) system, in which cellular user equipments (UEs) communicate with a baseband processing unit (BBU) through a set of distributed remote radio heads (RRHs) that are connected to the BBU via finite-capacity fronthaul links. The uplink of C-RAN system is interfered by a number of D2D communication links that operate in the same frequency band. Most of prior works on C-RAN uplink systems prescribe that the noise signals of different RRHs do not have statistical correlation. However, in the D2D underlaid C-RAN uplink systems, the effective noise signals that contain the D2D interference signals may have inter-RRH cross correlation. This work proposes an improved distributed reception scheme which leverages the noise correlation information. To tackle non-convex sum-rate maximization problem, a successive convex approximation (SCA) based iterative algorithm is derived that guarantees monotonically non-decreasing sum-rates. Some numerical results are presented that validate the advantages of the proposed scheme.

This work studies the uplink of a cloud radio access network (C-RAN) coexisting with a set of in-band device-to-device (D2D) communication links. In the system, the radio access communication from the user equipments (UEs), served by the C-RAN system, to the remote radio heads (RRHs) and the D2D communications take place at the same frequency band so that they interfere with each other while degrading the overall spectral efficiency performance. To mitigate this impact, the joint design of C-RAN uplink power control, D2D transmit power control as well as fronthaul quantization strategies is tackled with the goal of maximizing the sum-rate of all the UEs of the C-RAN and D2D communication systems. For the fronthaul quantization, both the Gaussian test channel and uniform scalar quantization approaches are considered. An iterative algorithm is derived based on the concave convex procedure (CCCP) approach to find an efficient solution, and numerical results are provided to examine the advantages of the proposed joint design as compared to the conventional separate optimization algorithms.