Random Matrix Methods for Wireless Communications: Estimation

In this paper, a deterministic equivalent of ergodic sum rate and an algorithm for evaluating the capacity-achieving input covariance matrices for the uplink large-scale multiple-input multiple-output (MIMO) antenna channels are proposed. We consider a large-scale MIMO system consisting of multiple users and one base station with several distributed antenna sets. Each link between a user and an antenna set forms a two-sided spatially correlated MIMO channel with line-of-sight (LOS) components. Our derivations are based on novel techniques from large dimensional random matrix theory (RMT) under the assumption that the numbers of antennas at the terminals approach to infinity with a fixed ratio. The deterministic equivalent results (the deterministic equivalent of ergodic sum rate and the capacity-achieving input covariance matrices) are easy to compute and shown to be accurate for realistic system dimensions. In addition, they are shown to be invariant to several types of fading distribution.

On Capacity of Large-Scale MIMO Multiple Access Channels with Distributed Sets of Correlated Antennas

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

/pdf/massive-mimo-transmission-for-leo-satellite-communications-49elabctuf.pdf

Massive MIMO Transmission for LEO Satellite Communications

Current access infrastructures are characterized by heterogeneity, low latency, high throughput, and high computational capability, enabling massive concurrent connections and various services. Unfortunately, this design does not pay significant attention to mobile services in underserved areas. In this context, the use of aerial radio access networks (ARANs) is a promising strategy to complement existing terrestrial communication systems. Involving airborne components such as unmanned aerial vehicles, drones, and satellites, ARANs can quickly establish a flexible access infrastructure on demand. ARANs are expected to support the development of seamless mobile communication systems toward a comprehensive sixth-generation (6G) global access infrastructure. This paper provides an overview of recent studies regarding ARANs in the literature. First, we investigate related work to identify areas for further exploration in terms of recent knowledge advancements and analyses. Second, we define the scope and methodology of this study. Then, we describe ARAN architecture and its fundamental features for the development of 6G networks. In particular, we analyze the system model from several perspectives, including transmission propagation, energy consumption, communication latency, and network mobility. Furthermore, we introduce technologies that enable the success of ARAN implementations in terms of energy replenishment, operational management, and data delivery. Subsequently, we discuss application scenarios envisioned for these technologies. Finally, we highlight ongoing research efforts and trends toward 6G ARANs.

Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure

Whenever multibeam satellite systems target very aggressive frequency reuse in their coverage area, inter-beam interference becomes the major obstacle for increasing the overall system throughput. As a matter of fact, users located at the beam edges suffer from a very large interference for even a moderately aggressive planning of reuse-2. Although solutions for inter-beam interference management have been investigated at the satellite terminal, it turns out that the performance improvement does not justify the increased terminal complexity and cost. In this article, we pay attention to interference mitigation techniques that take place at the transmitter (i.e. the gateway). Based on this understanding, we provide our vision on advanced precoding techniques and user clustering methods for multibeam broadband fixed satellite communications. We also discuss practical challenges to deploy precoding schemes and the support introduced in the recently published DVB-S2X standard. Future challenges for novel configurations employing precoding are also provided.

Precoding in Multibeam Satellite Communications: Present and Future Challenges

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive MIMO techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.

/pdf/massive-mimo-transmission-for-leo-satellite-communications-2hzkmfvhz2.pdf

Low earth orbit (LEO) satellite communication systems have attracted extensive attention due to their smaller pathloss, shorter round-trip delay and lower launch cost compared with geostationary counterparts. In this paper, the downlink transmit design for massive multiple-input multiple-output (MIMO) LEO satellite communications is investigated. First, we establish the massive MIMO LEO satellite channel model where the satellite and user terminals (UTs) are both equipped with the uniform planar arrays. Then, the rank of transmit covariance matrix of each UT is shown to be no larger than one to maximize ergodic sum rate, which reveals the optimality of single-stream precoding for each UT. The minorization-maximization algorithm is used to compute the precoding vectors. To reduce the computation complexity, an upper bound of ergodic sum rate is resorted to produce a simplified transmit design, where the rank of optimal transmit covariance matrix of each UT is also shown to not exceed one. To tackle the simplified precoder design, we derive the structure of precoding vectors, and formulate a Lagrange multiplier optimization (LMO) problem building on the structure. Then, a low-complexity algorithm is devised to solve the LMO, which takes much less computation effort. Simulation results verify the performance of proposed approaches.

Downlink Transmit Design in Massive MIMO LEO Satellite Communications

Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO downlink (DL) transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems by exploiting statistical channel state information (sCSI) at the transmitter. We first establish a massive MIMO channel model for LEO satellite communications and propose Doppler and time delay compensation techniques at user terminals (UTs). Then, we develop a closed-form low-complexity sCSI based DL precoder by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Motivated by the DL ASLNR upper bound, we further propose a space angle based user grouping algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems.

/pdf/leo-satellite-communications-with-massive-mimo-9f8cupod9m.pdf

LEO Satellite Communications with Massive MIMO

We investigate non-orthogonal unicast and multicast (NOUM) transmission for massive multiple-input multiple-output systems, where only statistical channel state information of all user terminals is available at the base station. We adopt a weighted sum of the achievable ergodic unicast rate and multicast rate as the design objective. We first show the closed-form eigenvectors of the optimal unicast and multicast transmit covariance matrices, respectively, which reveals the optimality of beam domain transmission and simplifies the large-dimensional matrix-valued NOUM transmission design into a beam domain power allocation problem. Via invoking the concave-convex procedure, we, then, propose an efficient iterative beam domain power allocation algorithm with guaranteed convergence to a stationary point. In addition, we derive the deterministic equivalent of the objective in each iteration to further reduce the optimization complexity. Simulation results show that the proposed NOUM transmission can provide a significant performance gain in terms of the achievable ergodic unicast-multicast rate region over the conventional orthogonal approach.

Xiqi Gao

Papers

Massive MIMO Transmission for LEO Satellite Communications

Massive MIMO Transmission for LEO Satellite Communications

Downlink Transmit Design in Massive MIMO LEO Satellite Communications

LEO Satellite Communications with Massive MIMO

Non-Orthogonal Unicast and Multicast Transmission for Massive MIMO With Statistical Channel State Information