Hybrid precoding is considered as a promising candidate to balance between the sum rate and the power and cost in the mmWave MIMO systems. However, it still suffers from huge power consumption due to considerable radio frequency links and phase shifters. In this study, an energy-efficient adaptive switching module (ASM) and phase modulation array (PMA) assisted hybrid precoding scheme is proposed, in which ASM is introduced to enhance the energy efficiency while PMA is employed to implement analog precoding to reduce power consumption. The ASM-PMA hybrid precoder design problem related to three matrix variables is formulated. Besides, the joint ASM-PMA hybrid precoder/combiner design at both transmitter and receiver is also investigated. Owing to the tough task of joint optimization of matrix variables, we can divide it into two sub-problems. Correspondingly, alternating optimization hybrid precoding (AOHP) algorithm is suggested to solve the PMA. Then, matching theory and convolutional neural network (CNN) based algorithms are respectively proposed to seek the optimal ASM. For the CNN based scheme, relaxed adaptive switching CNN (RAS-CNN) is designed to solve the binary integer problem in the offline train stage, while in the online deployment stage, ideal adaptive switching CNN (IAS-CNN) is adopted. Furthermore, CNN accepts estimated channel as input to yield robust precoding performance and lower computational complexity. Simulation results verify that, the proposed hybrid precoding can outperform other schemes in energy efficiency with satisfactory spectral efficiency.

The rapid development of wireless communication comes at the cost of dramatically increasing energy consumption, which makes the optimisation of energy efficiency (EE) extremely important. In this study, the authors focus on maximising EE of large‐scale multiple‐input–multiple‐output system by optimising the transmit power and the number of selected base station antennas. First, the EE optimisation problem, i.e. finding the optimal EE‐based values of each parameter, when the other one is fixed, is investigated. Then, the optimisation problem jointly considering these two parameters to maximise EE is studied. Moreover, the EE optimisation problem is extended to multi‐user (MU) system. Owing to the formulation of EE, the EE optimisation problem is reformulated as the fractional programming problem, and the Dinkelbach method is proposed to deal with this problem instead of the binary search algorithm (BSA) by switching the objective function to a weighted sum of rate and power. Simulation results show that the EE can be improved with the proposed Dinkelbach method, and it has a faster convergence speed than the BSA without any performance loss for both single‐user system and MU system.

Indoor localisation could benefit greatly from non‐line‐of‐sight (NLOS) identification and mitigation, since the major challenge for WiFi indoor ranging‐based localisation technologies is multipath and NLOS. NLOS identification and mitigation on commodity WiFi devices, however, are challenges due to limited bandwidth and coarse multipath resolution with mere MAC layer received signal strength index. In this study, the authors explore and exploit the finer‐grained PHY layer channel state information (CSI) to identify and mitigate NLOS. Key to the authors’ approach is exploiting several statistical features of CSI, which are proved to be particularly effective. The approaches, NLOS identification support vector machine (NISVM) and related channel information regression model (RCIRM), based on machine learning are proposed to identify NLOS and mitigate NLOS error, respectively. Experiment results in various indoor scenarios with severe interferences demonstrated an overall NLOS identification rate of 94.12% with a false alarm rate of 5.88% and a better mitigation performance.

Due to the high cost and power consumption, it is impractical to perform full‐digital beamforming at baseband in millimetre‐wave systems. Thus, as a cost‐effective alternative, a hybrid beamforming (HBF) is introduced in this study. Since the HBF can be designed as a matrix factorisation problem, an effective greedy algorithm with low complexity for multiuser scenes is proposed to approach the full‐digital matrix. Firstly, initialisation for partial columns of the analogue matrix is enabled by extracting the phases of the full‐digital scheme. Then the singular value decomposition in low‐dimension matrix can be used to obtain the rest columns of the analogue matrix. Finally, based on effective channel, the digital matrix can be easily designed. Therefore, the authors' proposed algorithm has low complexity and only needs fewer iterations. Furthermore, by handling the inter‐user interference, the full‐digital matrix needed for the initialisation in analogue domain can be approximated to an orthogonal matrix, which can further reduce computational complexity. Simulation results demonstrate that the proposed greedy algorithm performs very close to the optimal unconstrained solution and enjoys low complexity.

The cooperative spectrum sensing techniques in cognitive radio networks increase the energy consumption while improving the throughput of the system. The energy efficient techniques with sensing time optimization are considered in this paper. The optimization model is established, in which the sensing time is to be optimized to maximize the energy efficiency. Then, the golden section search algorithm is used to get the optimal solution based on the proof that energy efficiency is a unimodal function in terms of sensing time. The initial value of the golden section search algorithm is given, and the optimal value is obtained by iteration. Simulation results show that the energy efficiency can be improved obviously by optimizing sensing time.

In this paper, a linear Channel Orthogonalization (CO) algorithm with low complexity is presented, which is applicable to systems with an arbitrary number of antennas and users. Unlike other conventional linear methods, CO obtains the orthonormal basis to achieve the precoding matrix by means of using Schmidt Orthogonalization method. Then, the algorithm (CO-THP) combining CO with Tomlinson-Harashima Precoding (THP) is proposed to eliminate the residual interference from any former user in CO. Different from the conventional THP algorithms, CO-THP eliminates all the Multi-User Interference (MUI) without amplifying the noise, which brings it better BER performance. And the combination with THP brings CO a higher capacity than linear scheme BD. Simulation results show the performance advantage obtained by the CO and CO-THP.

This paper presents a novel reduced-complexity channel estimator for Coordinated Multi-point (CoMP) multi-user systems. The proposed scheme is based on Space Alternating Generalized-EM (SAGE) algorithm combining with the Least Square (LS) criterion. Aiming at the problem that the conventional joint multi-user LS estimator introduces the complex inversion of large size matrix when using non-orthogonal pilots, the proposed estimator avoids the inverse operation by transforming the joint multi-user estimation into a series of single-user estimating process in an iterative way. Simulation results verify that the proposed algorithm obtain huge complexity reduction, while achieving the optimal performance of joint LS channel estimation for the CoMP systems.

A novel decomposed transceiver design scheme is developed for multiuser non-regenerative MIMO relay downlink systems. With a decomposed relay structure, the whole system can be decomposed into a point-to-point and a point-to-multipoint systems, thus the transceiver design can be individually proceeded for the first and second hop. In the first hop, the transceiver is derived to minimize the mean square error (MSE) of symbol estimations with equal MSEs of all data streams. While for the second hop, a modified block diagonalization (BD) and geometric mean decomposition (GMD) based scheme is proposed to design the transceiver. Simulation results demonstrate the proposed method can effectively improve the BER performance.

The rapid growth of wireless networks makes cognitive coexistence an important technology to use the spectrum efficiently. In this paper, a novel sensing based delay optimal method is considered to meet the requirement of the delay-sensitive traffics as well as to minimize the collision between the primary user and the secondary user. After establishing the Markov Decision Process (MDP) model, we proposed a spectrum access policy using EXP-LOG rule, in which the transmission probability is determined by the sensing result and queue state information. The simulation results verify the validity and feasibility of the proposed policy.

In this paper, a low complexity Modified QRD-M (MQRD-M) algorithm based on limited tree search is proposed for MIMO systems. The sphere radius and candidate set of each branch are considered to reduce the computational complexity. The sphere radius is efficiently acquired through the QRD detection based on pruning off unnecessary survival branches at each layer. Moreover, the candidate set of each branch is used to cut down the metric computation for it is much smaller than modulation set. Simulation results show that the proposed MQRD-M algorithm can achieve near-MLD performance and its average metric computation is lower than conventional QRD-M algorithms.

A new grouping strategy based on particle swarm optimization (PSO) is proposed for the multiuser MIMO broadcast systems in this paper. Aiming at maximizing the system capacity, the PSO algorithm is introduced to seek the best users group. Besides, two objective functions in PSO applicable for the cases of different requirements on capacity and complexity are also presented, which can be denoted as PSO_I algorithm and PSO_II algorithm respectively. Simulation results reveal that the PSO_I strategy achieves nearly the same capacity performance to the optimal exhaustive search (ES) algorithm, and the PSO_II algorithm is slightly worse than PSO_I algorithm in terms of capacity performance, but with lower complexity.

In this paper, a novel scheduling strategy based on particle swarm optimization (PSO) algorithm is proposed for the downlink of multiuser MIMO systems. By means of PSO searching mechanism, this scheduling algorithm mainly seeks the optimal "particle", i.e. the best scheduled user group, to maximize the system capacity. Besides, a new approach to associate the possible scheduled candidates by "particles" in PSO is also presented. Simulation results reveal that the proposed PSO strategy achieves nearly the same performance to the exhaustive search (ES) algorithm in terms of capacity performance, but with lower complexity. The convergence analysis is also presented to give a guideline in determining the parameters to keep the PSO convergent.