Search

In this paper, we investigate the impact of user pairing on the power consumption of 2-user non-orthogonal multiple access (NOMA) systems in the downlink. We formulate the joint power allocation and user pairing problem as a mixed-integer programming problem with the objective of minimizing the total transmit power consumption. While the pairwise power allocation strategy is straightforward, for a system with $2K$ users and K NOMA pairs, there exist ${}\frac {(2K)!}{2^{K} \times K!}$ possible pairing strategies, resulting in a combinatorial search space that grows drastically with the number of users in the system. Hence, we propose an analytical approach to obtain the globally optimum user pairing strategy. Notably, our procedure has a linear time complexity of $\mathcal {O}(2K)$ , which is a significant improvement over the suboptimal and computationally expensive methods in the existing literature. We demonstrate through extensive simulations that the proposed optimal pairing strategy can attain considerable performance gains in terms of power savings compared to benchmark schemes. In particular, in a typical deployment environment, 63% of the total power budget is saved at a mean received signal-to-noise ratio (SNR) of 15.7 dB among the users. Finally, we evaluate the energy efficiency (EE) of NOMA transmission compared to the EE achieved through orthogonal multiple access (OMA) transmission. We demonstrate that the EE gain of NOMA transmission compared to OMA is improved more than sixfold at convergence by adopting the power minimization approach studied in this work, rather than adopting the sum rate maximization approach found in the literature.

Integrated access and backhaul (IAB) networks operating in full-duplex (FD) mode at millimeter wave frequencies have been actively investigated in the context of future-generation communications networks. However, conventional analog cancellation techniques cannot adequately mitigate the self-interference resulting from the FD operation and the multi-user interference. Hence, in this paper, we consider a multi-cell, multi-user IAB network and jointly design the beamforming and combining matrices to maximize the network’s weighted sum rate. Given the non-convex nature of the problem, we reformulate it using weighted minimum-mean-square-error (WMMSE) and extended fractional programming (FP) techniques followed by a block coordinate descent (BCD) approach. Extensive simulation results validate the superior performance of our proposed algorithms. Specifically, the WMMSE and FP methods can achieve 50 bits/sec/Hz higher than the benchmark scheme for a network employing three cells with two uplink and two downlink users per cell.

The sparsity of millimeter wave (mmWave) channels in angular and temporal domains has been exploited for channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage makes localization highly challenging, which may lead to big positioning inaccuracy. One promising solution is to employ reconfigurable intelligent surfaces (RIS) to generate virtual line-of-sight (VLoS) paths. Hence, it is essential to investigate the wireless positioning in RIS-aided mmWave systems. In this paper, an adaptive joint LoS and VLoS localization scheme is proposed, where the VLoS is constructed by a beamforming protocol operated between RIS and mobile station (MS). More specifically, to sense the location and orientation of a MS, a novel interlaced scanning beam sweeping algorithm is proposed to acquire the optimal beams. In this algorithm, LoS and VLoS paths are separately estimated and the optimal beams are selected according to the received signal strength so as to mitigate the LoS blockage problem. Then, based on the selected beams and received signal strength, angle of arrival (AoA), angle of departure (AoD), angle of reflection (AoR) and time of arrival (ToA) are estimated. Finally, with the aid of these estimated parameters, the location and orientation of the MS are estimated. We derive the Cramer-Rao lower bounds (CRLBs) for both location estimation and orientation estimation, and compare them with the corresponding results obtained from simulations. We compare the performance of our proposed scheme with that attained by three legacy schemes, when various aspects are considered. The performance results show the superiority of our proposed beam training algorithm, which is capable of achieving a localization error within 15 cm and an orientation error within 0.003 rads. Furthermore, the training overhead is 100 times less than that of the conventional exhaustive search algorithm, while obtaining a 13 dBm power gain when compared with the hierarchical codebook based search algorithm.

High speed trains (HST) have gradually become an essential means of transportation, where given our digital world, it is expected that passengers will be connected all the time. More specifically, the on-board passengers require fast mobile connections, which cannot be provided by the currently implemented cellular networks. Hence, in this article, we propose an analogue radio over fiber (A-RoF) aided multi-service network architecture for high-speed trains, in order to enhance the quality of service as well as reduce the cost of the radio access network (RAN). The proposed design can simultaneously support sub-6GHz as well as millimetre wave (mmWave) communications using the same architecture. Explicitly, we design a photonics aided beamforming technique in order to eliminate the bulky high-speed electronic phase-shifters and the hostile broadband mmWave mixers while providing a low-cost RAN solution. Finally, a beamforming range of 180° is demonstrated with a high resolution using our proposed system.

In order to reduce the hardware complexity and cost of mmWave transceivers, hybrid beamforming techniques have been developed, which rely on the channel state information (CSI) available to the receiver and/or transmitter. In mmWave channel estimation, the compressed sensing (CS)-based algorithms like orthogonal matching pursuit (OMP) have been widely studied to take the advantages of the sparse characteristics of mmWave channels. Specifically, the OMP-assisted adaptive codebook channel estimation has the merit of reduced implementation complexity, but it performs undesirably in low signal to noise ratio (SNR) scenarios. To circumvent this problem, in this paper, we develop an improved adaptive codebook channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) mmWave systems, which enhances the estimation performance by exploiting the multi-carrier signals for joint decision making. Our studies show that the proposed channel estimation is capable of significantly improving the estimation accuracy at low SNR, while enjoying a low complexity for implementation.

Benefiting from the high resolution in beamspace, millimeter wave (mmwave) communication has been regarded as a high-accuracy localization solution, where the location information is embedded in the channel via angle and time delay, for example. In this paper, to locate a user equipment (UE) and scatterers, we present the localization model in mmwave communications as a compressed sensing assisted channel estimation problem, which is solved using a proposed two-stage channel estimation based localization scheme. During the first stage, a sparse Bayesian learning (SBL) algorithm is operated to attain a coarse estimation. Then during the second stage, a multi-stage grid refinement assisted fine estimation is achieved by a distributed compressed sensing simultaneous orthogonal matching pursuit (DCS-SOMP) algorithm. Moreover, in our approach, the few-bit analog to digital converters (ADCs) are utilized by the receiver of UE so as to attain a good trade-off among performance, complexity and energy-efficiency. Finally, the performance of channel estimation and positioning is comprehensively investigated and compared. It can be shown that our proposed two-stage approach is capable of achieving centimeter-level accuracy with the required number of quantization bits of ADCs less than four.

In this article, we propose an orthogonal frequency-division multiplexing system supported by the compressed sensing assisted index modulation, termed as (OFDM-CSIM), applied to millimeter-wave (mmWave) communications. In the OFDM-CSIM mmWave system, information is conveyed not only by the classic constellation symbols but also by the on/off status of subcarriers, where the size of constellation symbols and the number of active subcarriers can be beneficially configured for maximizing the system's throughput. We conceive a machine learning (ML) assisted adaptive OFDM-CSIM mmWave system, which simultaneously benefits from the OFDM with index modulation (IM), compressed sensing (CS) and the hybrid beamforming techniques. Specifically, a ML-assisted link adaptation scheme is designed based on the k -nearest neighbors (k -NN) algorithm with the objective to maximize the system's throughput. Our studies show that the proposed ML-assisted link adaptation is capable of providing higher throughput than the conventional threshold-based link adaptation when different antenna structures are considered. Furthermore, the achievable data rates of four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform circle planar array (UCPA) and uniform cylindrical array (UCYA), are investigated and compared over mmWave channels. The simulation results show that the UCYA achieves the highest data rate among these antenna arrays.