In this paper, we investigate the impedance matching problem for realistic arrays of lossy and mutually coupled antennas, aiming to enhance the beamforming performance of the system. We develop a novel method to optimize the characteristic and load impedances in the transmit circuit that drives the antenna array. Numerical results are provided to show that the developed method has a faster convergence speed with considerably lower computational complexities than an existing impedance optimization method, while it can achieve almost the same favorable performance as the latter.

Cellular communication systems are currently evolving to provide non-terrestrial networks (NTNs) connectivity. However, due to large link path loss and realistic antenna capabilities of commercial smartphones, direct access to low Earth orbiting (LEO) satellites can so far only provide very small data rates. In this paper, we investigate the potential in using multi-dimensional constellations (MDC) for improving NTN connectivity. MDC allows non-coherent detection over coherent fading blocks, which can reduce the pilot overhead for channel estimation and remove sensitivity to channel estimation quality in low signal-to-noise ratio links. Our simulation results show that the considered MDC scheme can almost double the data rate compared to a NR-type scheme in the considered coverage-limited LEO scenario of small transport block transmission under NTN line-of-sight channel.

Extended Reality (XR) has attracted great attention from both academic and industry, for providing users with an immersive experience anywhere. Nowadays, XR video streaming service is evolving to high definition (HD), which results in massive data traffic with more stringent latency requirement. Due to the two characteristics, it is challenging to support the commercial use for XR service in the current New Ratio (NR) network. In this paper, we propose a real-time super-resolution (RTSR) framework for XR HD video transmission. The basic idea is to utilize the overfitting feature of Deep Neural Network (DNN) to learn the non-linear mapping between low-definition (LD) video frames and HD video frames. The cloud XR server can transmit the LD frames together with the dedicated super resolution (SR) models instead of sending HD frames directly. The receiver can recover the HD frames locally with the inferencing ability of SR model. In addition, by introducing the online training and layered transmission strategy, the SR model update period can be adaptively adjusted according to the scenario changes, which also reduces the transmission overhead. Simulation results demonstrate the superiority of our proposed RTSR, which can save up to 50% traffic and increase the XR capacity about 40% compared with the conventional SR scheme. In terms of the system capacity, our results show that the average number of UEs can reach about 23 per cell under the common settings of Dense Urban.

Extended reality (XR) is one of the most important media applications enabled by 5G and beyond. Providing XR services over 5G networks is very challenging due to high requirements in terms of data rate, reliability, and latency. Different from eMBB and URLLC services, XR has a unique characteristic that each XR video frame can be segmented into several correlated packets. Any one of the packets loss would result in frame decoding error, and thus bad quality of experience. In this paper, we investigate such characteristic of frame-level integrity and propose a new concept called frame-level integrated transmission. Unlike conventional wireless communication with the goal of transmitting one packet successfully, the proposed frame-level integrated transmission takes “multiple packets belonging to one video frame” as an “entity”. Specifically, we formulate a resource allocation problem by considering the frame-level integrity to maximize the number of satisfied UEs under the data rate, reliability, and latency requirements for each UE. This problem is highly challenging due to the discontinuous and non-convex objective function and intractable reliability constraint. We propose to solve it approximately by jointly considering the user admission control and a frame-level integrated transmission problem to maximize the total number of frames that are successfully transmitted, which is efficiently solved via smooth function approximation and convex-concave procedure. Simulation results demonstrate the superiority of the proposed frame-level integrated transmission scheme, which can achieve much better performance than the conventional proportional fairness scheduler in terms of various performance metrics.

Recently, eXtended Reality (XR) has gained significant attention for providing immersive user experience in various applications with the help of 5G new radio (NR) network. To ensure the quality of experience (QoE) in XR services, wireless transmission plays an important role. In this paper, we propose a novel performance metric that can reflect the impact of network transmission on XR services, naming it as XR Quality Index (XQI). Specifically, fine-grained and coarse-grained XQI models are provided with detailed calculation procedures. The aim of XQI is to produce a final score that can reflect the impact of network transmission on QoE, and then could be used for network optimization. Simulation results verify the effects of XQI in evaluating the quality of XR videos.

Full-duplex self-backhauling is promising to provide cost-effective and flexible backhaul connectivity for ultra-dense wireless networks, but also poses a great challenge to resource management between the access and backhaul links. In this paper, we propose a user-centric joint access-backhaul transmission framework for full-duplex self-backhauled wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by multiple small base stations (SBSs). In the backhaul link, user-centric multicast transmission is proposed so that each user’s message is treated as a common message and multicast to its serving SBS cluster. We first formulate an optimization problem to maximize the network weighted sum rate through joint access-backhaul beamforming and SBS clustering when global channel state information (CSI) is available. This problem is efficiently solved via the successive lower-bound maximization approach with a novel approximate objective function and the iterative link removal technique. We then extend the study to the stochastic joint access-backhaul beamforming optimization with partial CSI. Simulation results demonstrate the effectiveness of the proposed algorithms for both full CSI and partial CSI scenarios. They also show that the transmission design with partial CSI can greatly reduce the CSI overhead with little performance degradation.

In-band full-duplex (IBFD) self-backhauling is promising to provide cost-effective and flexible backhaul connectivity in ultra-dense wireless networks. In this paper, we propose a user-centric joint access-backhaul transmission framework for performance optimization in IBFD-enabled self- backhauled cooperative wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by a cluster of small base stations (SBSs) via joint beamforming. In the backhaul link, user-centric multicast transmission is proposed so that the macro base station (MBS) treats each user's data as a multicast message and sends it to its serving SBS cluster. We formulate an optimization problem to maximize the end-to-end weighted sum rate of all users under power constraints through joint design of multicast beamforming in the backhaul link as well as SBS clustering and beamforming in the access link. We first tackle the joint access- backhaul beamforming problem under given SBS clustering by transforming it into a manifold optimization problem, for which a stationary solution is obtained using the Riemannian optimization technique. We then develop a heuristic algorithm to determine the SBS clustering. Simulation results demonstrate the effectiveness of the proposed algorithms.

This paper investigates the transmission design in fog radio access networks where each edge node (EN) has a local cache and can pre-store contents based on partition-based caching. In the considered partition- based caching, each file is partitioned into multiple subfiles and cached at different ENs. Upon user requests, the ENs can jointly transmit all the subfiles to the target users simultaneously. Each user adopts a successive interference cancellation receiver to decode each desired subfile with certain order. To improve the network performance, we propose a novel cache-aware scheme to determine the decoding order. Furthermore, we formulate a joint beamforming and dynamic EN clustering optimization problem to minimize the weighted sum of transmission power cost and fronthaul cost under the quality-of-service constraint for each user. An efficient algorithm by adopting smooth function approximation and convex-concave procedure is proposed to solve this problem. To exploit the advantages of partition-based caching, we further design a user-aware caching strategy. Numerical results show that the proposed transmission scheme together with the proposed caching strategy can strike a better balance between power consumption and fronthaul consumption than existing schemes.

The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this paper, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and unicast services concurrently in cooperative multi-cell cellular networks with a limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast message first, subtracts it, and then decodes its dedicated unicast message. We formulate a joint multicast and unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-unicast rate region.

The demand for providing multicast services in emerging cellular networks is increasing. This paper proposes a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to incorporate multicast and unicast services into cellular networks with limited- capacity backhaul. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for unicast services, and the two layers are superposed with different beamformers and decoded by each user receiver using successive interference cancellation. To optimize the non- orthogonal transmission, we formulate a joint multicast and unicast beamforming design problem with adaptive base station (BS) clustering that aims to maximize the weighted sum of multicast rate and unicast rate under peak power constraints and peak backhaul constraints on each BS. The problem is a sparse optimization problem and NP- hard. By means of smoothed l0-norm approximation and novel algebraic operations, we transform it into a difference of convex (DC) programming, which is solved using convex-concave procedure (CCP) with guaranteed convergence. Simulation results demonstrate the superiority of the proposed algorithm, especially when the backhaul constraint is not too stringent. Results also show that the proposed LDM-based non-orthogonal scheme can achieve a much larger multicast-unicast rate region than orthogonal schemes.

Multi-group multicast beamforming in wireless systems with large antenna arrays and massive audience is investigated in this paper. Multicast beamforming design is a well-known non-convex quadratically constrained quadratic programming (QCQP) problem. A recent attempt is to apply convex-concave procedure (CCP) to find a stationary solution, whose complexity, however, increases dramatically as the problem size increases. In this paper, we propose a low-complexity highperformance algorithm for multi-group multicast beamforming design in large-scale wireless systems by utilizing the alternating direction method of multipliers (ADMM) together with CCP. In specific, the original non-convex QCQP problem is first approximated by a sequence of convex subproblems via CCP. Each convex subproblem is then reformulated as a novel ADMM form. Our ADMM reformulation enables that each updating step is performed by solving multiple small-size subproblems with closed-form solutions in parallel. Numerical results show that our fast algorithm maintains the same favorable performance as state-of-the-art algorithms but reduces the complexity by orders of magnitude.

This paper studies multi-group multicast beamforming with a hybrid large-scale antenna array in millimeter wave (mmWave) communication systems. A low-complexity hybrid structure is adopted, where each RF chain is only connected to part of the antenna elements. We formulate a hybrid analog and digital beamforming design problem for multi-group multicast transmission with the objective of minimizing the total transmit power at the base station, subject to an individual signal-to-interference-plus-noise ratio constraint for each multicast group. The problem is very challenging and its global optimal solution is difficult to obtain. We first adopt alternating minimization method to design the analog and digital beamformer alternatively. Then we solve each of the analog and digital subproblems through solving a sequence of convex problems via concave-convex procedure (CCP). Each convex CCP subproblem is reformulated as a novel alternating direction method of multipliers (ADMM) form. Our ADMM reformulation enables that each updating step can be decomposed into multiple subproblems with much smaller size, which can be solved optimally in parallel with closed-form expressions. Simulation results show that our algorithm can achieve favorable performance with very low complexity compared with the state-of-art methods.

In this paper, we investigate cooperative caching for spectrum access in cognitive radio networks. By cooperative caching, we mean that the unlicensed secondary base station (SBS) can cache certain primary contents to serve primary users, in exchange for the opportunities to access the licensed spectrum. We consider the joint optimization of caching and scheduling of the SBS to maximize the weighted average number of satisfied secondary requests under the average available time constraint and the cache capacity constraint. This problem is a mixed-integer bilinear programming, which is challenging in general. By exploring the special structure of the problem, we first show that the optimal caching satisfies a cache-split structure and the optimal scheduling satisfies a rate-ratio structure. Then, based on these optimality properties, we transform the original problem into a simplified joint cache splitting and SU partitioning optimization problem, and propose an efficient algorithm to solve it optimally. Moreover, we investigate the impacts of the primary and secondary content popularity distributions on the system performance. Numerical results verify the theoretical analysis and provide some counter-intuitive insights.

Multi-group multicast beamforming in wireless systems with large antenna arrays and massive audience is investigated in this paper. Multicast beamforming design is a well-known non-convex quadratically constrained quadratic programming (QCQP) problem. A conventional method to tackle this problem is to approximate it as a semi-definite programming problem via semi-definite relaxation, whose performance, however, deteriorates considerably as the number of per-group users goes large. A recent attempt is to apply convex-concave procedure (CCP) to find a stationary solution by treating it as a difference of convex programming problem, whose complexity, however, increases dramatically as the problem size increases. In this paper, we propose a low-complexity high-performance algorithm for multi-group multicast beamforming design in large-scale wireless systems by leveraging the alternating direction method of multipliers (ADMM) together with CCP. In specific, the original non-convex QCQP problem is first approximated as a sequence of convex subproblems via CCP. Each convex subproblem is then reformulated as a novel ADMM form. Our ADMM reformulation enables that each updating step is performed by solving multiple small-size subproblems with closed-form solutions in parallel. Numerical results show that our fast algorithm maintains the same favorable performance as state-of-the-art algorithms but reduces the complexity by orders of magnitude.

This paper presents a content-centric transmission design in a cloud radio access network by incorporating multicasting and caching. Users requesting the same content form a multicast group and are served by a same cluster of base stations (BSs) cooperatively. Each BS has a local cache, and it acquires the requested contents either from its local cache or from the central processor via backhaul links. We investigate the dynamic content-centric BS clustering and multicast beamforming with respect to both channel condition and caching status. We first formulate a mixed-integer nonlinear programming problem of minimizing the weighted sum of backhaul cost and transmit power under the quality-of-service constraint for each multicast group. Theoretical analysis reveals that all the BSs caching a requested content can be included in the BS cluster of this content, regardless of the channel conditions. Then, we reformulate an equivalent sparse multicast beamforming (SBF) problem. By adopting smoothed ℓ0-norm approximation and other techniques, the SBF problem is transformed into the difference of convex programs and effectively solved using the convex-concave procedure algorithms. Simulation results demonstrate significant advantage of the proposed content-centric transmission. The effects of heuristic caching strategies are also evaluated.

Multicast transmission and wireless caching are effective ways of reducing air and backhaul traffic load in wireless networks. This paper proposes to incorporate these two key ideas for content-centric transmission in a cloud radio access network (RAN) where multiple base stations (BSs) are connected to a central processor (CP) via finite-capacity backhaul links. Each BS has a cache with finite storage size and is equipped with multiple antennas. The BSs cooperatively transmit contents, either stored in the local cache or fetched from the CP, to multiple users in the network. Users requesting a same content form a multicast group and are served by a same cluster of BSs cooperatively using multicast beamforming. Assuming fixed cache placement, this paper investigates the joint design of multicast beamforming and content-centric BS clustering by formulating an optimization problem of minimizing the total network cost under the quality-of-service (QoS) constraints for each multicast group. The network cost involves both the transmission power and the backhaul cost. We model the backhaul cost using the mixed ℓ0/ℓ2-norm of beamforming vectors. To solve this non-convex problem, we first approximate it using the semidefinite relaxation (SDR) method and concave smooth functions. We then propose a difference of convex functions (DC) programming algorithm to obtain suboptimal solutions and show the connection of three smooth functions. Simulation results validate the advantage of multicasting and show the effects of different cache size and caching policies in cloud RAN.