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In order to address the shortcomings of orthogonal frequency division multiplexing (OFDM) and extend the lifetime of energy-constrained Internet-of-Things (IoT) devices, the combination of filter bank multi-carrier (FBMC) and simultaneous wireless information and power transfer (SWIPT) is investigated in this paper. Specifically, a multi-user FBMC-based SWIPT system is proposed in which user nodes (UNs) have the capability for both energy harvesting (EH) and information decoding (ID) with the aid of separate antennas. A practical non-linear EH model, which considers the saturation effects of the EH circuit, is considered. The information receiver at both the UNs and base station (BS) adopts an iterative interference cancellation (IIC) receiver to cancel the intrinsic interference in the demodulated FBMC signal. A sum-rate maximization problem is solved to jointly optimize parameters such as time, power, and weight allocations. Sub-optimal schemes are proposed for comparison. Numerical results show that the optimal solution significantly outperforms the sub-optimal methods in terms of achievable sum-rate and amount of harvested energy. Moreover, the results show that the proposed algorithm converges within a few iterations.

In this paper, we propose a cooperative distributed framework to optimize a variety of rate and energy-efficiency (EE) utility functions, such as the minimum-weighted rate or the global EE, for the K-user interference channel. We focus on the single-input multiple-output (SIMO) case, where each user, based solely on local channel state information (CSI) and limited exchange information from other users, optimizes its transmit power and receive beamformer, although the framework can also be extended to the multiple-output multiple-input (MIMO) case. The distributed framework combines an alternating optimization approach with majorization-minimization (MM) techniques, thus ensuring convergence to a stationary point of the centralized cost function. Closed-form power update rules are obtained for some utility functions, thus obtaining very fast convergence algorithms. The receivers treat interference as noise (TIN) and apply the beamformers that maximize the signal-to-interference-plus-noise (SINR). The proposed cooperative distributed algorithms are robust against channel variations and network topology changes and, as our simulation results suggest, they perform close to the centralized solution that requires global CSI. As a benchmark, we also study a non-cooperative distributed framework based on the so-called "signal-to-leakage-plus-noise ratio" (SNLR) that further reduces the overhead of the cooperative version. The work of Ignacio Santamaria was supported in part by the Ministerio de Ciencia e Innovación (Gobierno de España) / Agencia Española de Investigación (AEI) / FEDER funds of the European Union (EU) under Grant PID2019-104958RB-C43 (ADELE).

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations.

Multi-tier cloud-radio access networks (C-RANs) have been suggested as a promising network model in fifth generation wireless networks to provide high data rate, high spectral efficiency, and high energy efficiency at low cost. However, to achieve the potential benefits of multi-tier C-RANs, it is important to design efficient resource allocation algorithms provided that the data rate of users is guaranteed. Therefore, in this paper, we considered the joint optimization of remote-radio-heads (RRH) association, sub-channel assignment, and power allocation for network sum-rate maximization in single-carrier frequency division multiple access (SC-FDMA)-based multi-tier C-RAN. Due to the exclusivity and adjacency constraints of SC-FDMA, the resource allocation problem becomes harder to solve, thus, the optimal solution is difficult for reasonably sized network. Therefore, we propose an iterative algorithm that solves this non-linear mixed-integer problem in two steps wherein the first step, power allocation and subchannel assignment are carried out, while the second step of the proposed algorithm is concerned with the RRH-association. The iterative algorithm converges to efficient solution. The simulation results verify the effectiveness of our proposed algorithm.

In this paper, we study an energy-harvesting cellular two-way relay network with massive multiple-input multiple-output, where multiple energy-harvesting mobile stations (MSs) communicate with a base station (BS) via an energy-harvesting relay station (RS). A signal space alignment (SSA) based simultaneous wireless information and power transfer (SWIPT) protocol is proposed for efficient energy harvesting and information exchange. We analyze the performance of the proposed SSA-SWIPT scheme, and derive closed-form expressions for the asymptotic signal-to-interference-plus-noise ratios and the achievable sum-rate when the numbers of antennas at the BS and the RS go large simultaneously. We also investigate the optimal power splitting ratios at the RS and the MSs to maximize the achievable sum-rate. Numerical results are provided to verify the analysis and it is shown that employing massive antennas can improve the efficiencies of wireless energy harvesting and information transmission.

We study the sum-rate maximization problem, under a total power budget, for asynchronous single-carrier bi-directional relay networks , consisting of two transceivers and multiple amplify-and-forward relays. When different transceiver-relay links cause significantly different propagation delays in the signal they convey, the end-to-end channel is not amenable to a frequency-flat modeling; rather, a multi-path channel model is appropriate. Such a multi-path channel model results in inter-symbol-interference at the transceivers. Aiming to maximize the sum-rate of this channel over the relay weights and transceivers’ powers, we rigorously prove that such a sum-rate maximization problem leads to a relay selection scheme, where only those relays, which contribute to one of the taps of the end-to-end channel impulse response (CIR), are turned on. Indeed, we prove that the optimal end-to-end CIR has only one non-zero tap, rendering the end-to-end channel frequency-flat. Our proof shows that the mean-squared-error (MSE) optimal joint post-channel equalization, network beamforming, and power allocation scheme is sum-rate-optimal. The equivalence of MSE-optimal and sum-rate-optimal solutions is interesting, as MSE minimization promotes end-to-end reliability, while sum-rate maximization advocates for multiplexing gain. These approaches often pull the design of communication systems in different directions. For the aforementioned scenario, these approaches are identical as we prove.