The ultra-dense network (UDN) has been considered as a promising candidate for future 5G networks to meet the explosive data demand. To realize UDN, a reliable, gigahertz bandwidth, and cost-effective backhaul connecting ultradense small-cell BSs and macrocell BS are prerequisite. Millimeter-wave can provide the potential gigabit-per-second traffic for wireless backhaul. Moreover, mmWave can easily be integrated with massive MIMO for improved link reliability. In this article, we discuss the feasibility of mmWave massive-MIMO-based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. In particular, we propose a digitally controlled phase shifter network (DPSN)-based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of the mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of a transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macrocell BS can simultaneously support multiple small-cell BSs with multiple streams for each small-cell BS, which is essentially different from conventional hybrid precoding/combining schemes, typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospects to enable mmWave massive-MIMO-based wireless backhaul for 5G UDN are discussed.

This paper studies the impact of an intelligent reflecting surface (IRS) on computational performance in a mobile edge computing (MEC) system. Specifically, an access point (AP) equipped with an edge server provides MEC services to multiple internet of thing (IoT) devices that choose to offload a portion of their own computational tasks to the AP with the remaining portion being locally computed. We deploy an IRS to enhance the computational performance of the MEC system by intelligently adjusting the phase shift of each reflecting element. A joint design problem is formulated for the considered IRS assisted MEC system, aiming to optimize its sum computational bits and taking into account the CPU frequency, the offloading time allocation, transmit power of each device as well as the phase shifts of the IRS. To deal with the non-convexity of the formulated problem, we conduct our algorithm design by finding the optimized phase shifts first and then achieving the jointly optimal solution of the CPU frequency, the transmit power and the offloading time allocation by considering the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions. Numerical evaluations highlight the advantage of the IRS-assisted MEC system in comparison with the benchmark schemes.

Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

This work presents a potential solution for enabling the use of multicast in the 5G New Radio Release 17, called 5G NR Mixed Mode. The proposed multicast/broadcast mode follows one of the two approaches envisaged in 3GPP, which enables a dynamic and seamless switching between unicast and multicast, both in the downlink and the uplink. This paper also provides a performance evaluation of several IMT-2020 KPIs, including available data rate and spectral efficiency, user and control plane latencies, energy efficiency, and mobility, highlighting the potential advantages of this solution over unicast in relevant scenarios. Finally, other multipoint-based KPIs such as coverage or packet loss rate are also evaluated by means of system level simulations.

In this article, we investigate a resource allocation problem for multicarrier multiuser MISO (multiple-input-Single-output) downlink systems, where multiple co-channel multicast groups are served simultaneously. We consider a rate-splitting transmission scheme to address the inevitable inter-group interference under an overloaded multigroup multicast scenario, where the insufficient number of transmit antennas prevents the conventional schemes from neutralizing the interference. We first formulate an optimization problem for maximizing the minimum multicast group rate among all groups on all available subcarriers. This problem involves a joint power and subcarrier allocation optimization, and is non-convex. We apply an iterative scheme based on successive convex approximation (SCA) to find the locally optimal solution. Simulation results demonstrate the performance gain of the proposed scheme compared to the state-of-the-art transmission schemes.