The study of frequency-selective hybrid precoding for broadband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems has recently attracted major research interest. In this paper, we seek to provide an efficient solution to simultaneously conduct user scheduling and frequency-selective hybrid precoding for a broadband multiuser mmWave MIMO system. More specifically, we re-formulate the issue using the framework of stochastic-optimization. By utilizing the powerful tool of Lyapunov optimization, a dynamic weighted sum-rate maximization problem is formulated, towards which we develop an algorithm that simultaneously conducts i) user scheduling, ii) multi-carrier resource allocation, and iii) frequency-selective precoder design. It is also demonstrated that the proposed framework is capable of asymptotically optimizing a given stochastic utility (e.g. long-term sum rate). Simulation results are provided to demonstrate the superiority of the proposed algorithm.

Deep learning has a wide application in the area of natural language processing and image processing due to its strong ability of generalization. In this paper, we propose a novel neural network structure for jointly optimizing the transmitter and receiver in communication physical layer under fading channels. We build up a convolutional autoencoder to simultaneously conduct the role of modulation, equalization, and demodulation. The proposed system is able to design different mapping scheme from input bit sequences of arbitrary length to constellation symbols according to different channel environments. The simulation results show that the performance of neural network-based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate under fading channels. The proposed system can also be combined with other coding techniques to further improve the performance. Furthermore, the proposed system network is more robust to channel variation than traditional communication methods.

Hybrid precoding is introduced to millimeter wave (mmWave) multiple-input multiple-output (MIMO) System in order to reduce the number of radio frequency (RF) chains. In traditional hybrid precoding scheme, the maximum number of independent data streams is restricted by the number of RF-chains, which limits the achievable spectral efficiency (SE) rate. To further improve the SE rate, the generalized spatial modulation (GenSM) structure is considered. However, current gradient ascent iteration (GAI) algorithm of GenSM aided hybrid precoding is too complicated when the antenna array is large. In this paper we propose a novel low complexity algorithm for the analog and digital precoder design under GenSM hybrid precoding structure. Our approach gives additional unitary constraint to the digital precoder, formulating its design as a masked orthonormal procrustes problem. With this simplification, we accomplish the whole hybrid precoder design via turbo optimization (TO). Finally, numerical simulations are demonstrated to compare the achievable SE performance between our proposed low complexity scheme and some traditional hybrid precoding algorithms including the GAI algorithm.

The layered division multiplexing (LDM) is recently combined with spatial modulation (SM) systems to provide a more efficiency way for broadcasting transmission. In SM aided LDM (SM-LDM) systems, the service of each layer, which is allocated with different power, is transmitted via SM scheme. In this paper, a gradient descent based iterative method is proposed to optimize the injection level, which can enhance the spectral efficiency (SE) in the two-layer SM-LDM systems with maximum ratio combining (MRC). In addition, the concavity analysis of this optimization problem is also conducted. Monte Carlo simulations are also provided to verify the effectiveness of our proposed injection level optimization method.

As a combination of spatial modulation (SM) system and massive multiple‐input multiple‐output (MIMO) system, massive SM aided MIMO (SM‐MIMO) system is recently proposed. In broadband scenarios, single‐carrier (SC) modulation is introduced to massive SM‐MIMO system, thus massive SC‐SM MIMO system is proposed for uplink multi‐user transmission over frequency‐selective fading channels. In this study, the uplink spectral efficiency (SE) of multi‐cell massive SC‐SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi‐cell massive SC‐SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed‐form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

The application of hybrid precoding in millimeter wave (mm-wave) multiple-input multiple-output (MIMO) systems has been proven effective for reducing the number of radio frequency (RF) chains. However, the maximum number of independent data streams is conventionally restricted by the number of RF chains, which limits the spatial multiplexing gain. To further improve the achievable spectral efficiency (SE), in this paper we propose a generalized spatial modulation aided mmwave MIMO system to convey an extra data stream via the index of the active antennas group, while no extra RF chain is required. Moreover, we propose a hybrid analog and digital precoding scheme for SE maximization. Based on a narrowband mmwave-MIMO channel assumption, a closed-form lower bound is derived to quantify the achievable SE of the proposed system. By utilizing this lower bound as the cost function, a two-step algorithm is proposed to optimize the hybrid precoder. The proposed algorithm not only utilizes the concavity of the cost function over the digital power allocation vector but also invokes the convex 1∞relaxation to handle the non-convex constraint imposed by analog precoding. Finally, the proposed scheme is shown via simulations to outperform state-of-the-art mm-wave MIMO schemes in terms of achievable SE.

Multiple-input multiple-output (MIMO) techniques have constituted a key approach to facilitate the rapid data traffic growth in future 5G communication networks. However, the high cost and low efficiency nature of radio frequency (RF) resource has unfortunately reduced the benefit of MIMO. While spatial modulation (SM) technique has recently been proposed to reduce the RF resource for conventional small-scale MIMO systems, it is unclear if future 5G communication network, where conventional MIMOs encounter much different challenges, still embraces the benefits of SM. In order to address this issue, in this paper the application of SM in 5G communications is discussed, where we propose to incorporate SM into i) massive MIMO systems, and ii) millimeter wave (mmWave) MIMO systems, to strike a better tradeoff between energy efficiency and spectral efficiency for future wireless communications. It is revealed in this paper that SM is capable of improving the transmission data rates for 5G communication networks while maintaining an equal scale of RF resource occupancy, which therefore motivates the fruitful application of SM in 5G communications.

In this paper, a novel generalized spatial modulation (GenSM)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system is proposed for indoor transmission over line-of-sight (LoS) channels. Unlike the conventional GenSM-based mmWave systems, the application of digital beamforming (DBF) and the impact of spatial-domain codebooks are explored in our work. More specifically, we first propose a closed-form lower bound for quantifying the achievable spectral efficiency (SE) of our system. Then we seek to optimize the spatial-domain codebook and DBF design with respect to SE maximization. Lastly, numerical simulations are provided to substantiate the performance gain achieved by the proposed system compared against the conventional spatial multiplexing (SMX) MIMO schemes with waterfilling precoding.

Due to the high cost and low energy efficiency of the dedicated radio frequency (RF) chains, the number of RF chains in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) system is usually limited from a practical point of view. In this case, the maximum number of independent data streams is also restricted by the number of RF chains, which consequently leads to limiting the potentially attainable spatial multiplexing gain. In order to address this issue, in this paper, a novel generalized spatial modulation (GenSM) aided mmWave MIMO system is proposed, which enables the transmission of an extra data stream via the index of the active antennas group and requires no extra RF chain. Moreover, a two-step algorithm is also proposed to optimize the hybrid precoder design with respect to spectral efficiency (SE) maximization. Finally, numerical simulation results demonstrate the superior SE performance achieved by the proposed scheme.

Flat-fading channel models are usually invoked for analyzing the performance of massive spatial modulation multiple-input multiple-output (SM-MIMO) systems. However, in the context of broadband SM transmission, the severe inter-symbol-interference (ISI) caused by the frequency-selective fading channels can not be ignored, which leads to very detrimental effects on the achievable system performance, especially for single-carrier SM (SC-SM) transmission schemes. To the best of the author's knowledge, none of the previous researchers have been able to provide a thorough analysis on the achievable spectral efficiency (SE) of the massive SC-SM MIMO uplink transmission. In this context, the uplink SE of single-cell massive SC-SM MIMO system is analyzed, and a tight closed-form lower bound is proposed to quantify the SE when the base station (BS) uses maximum ratio (MR) combining for multiuser detection. The impacts of imperfect channel estimation and transmit antenna (TA) correlations are all considered. Monte Carlo simulations are performed to verify the tightness of our proposed SE lower bound. Both the theoretical analysis and simulation results show that the SE of uplink single-cell massive SC-SM MIMO system has the potential to outperform the uplink SE achieved by single-antenna UEs.

In generalised spatial modulation (GenSM) systems, the total number of active antenna groups is always not a power of two, which leaves room for designing an active antenna group (AAG) selection algorithm to achieve the constellation shaping gain and to improve the symbol error rate (SER). In this paper, based on uniform-cost search, a novel iterative AAG selection algorithm for GenSM systems is proposed with channel state information at the transmitter (CSIT). Based on the principle of minimum Euclidean distance maximization, the proposed algorithm reduces the computational complexity while maintaining the global optimality in improving SER comparing with exhaustive search algorithm. Monte Carlo simulations are performed to verify the global optimality in SER performance and favorable reduction in computational complexity striked by the proposed AAG selection algorithm.

Channel estimation for underwater acoustic (UWA) channels in orthogonal frequency division multiplexing (OFDM) systems is one of the most difficult problems in UWA communications. Because UWA channels are both frequency-selective and time-selective, the total number of channel coefficients to be estimated will significantly increase. Besides, the fast time-varying characteristics and strong Doppler effects will deteriorate the orthogonality between the OFDM subcarriers and cause inter-carrier interference (ICI). To handle these problems, in this paper, we firstly transform the discrete doubly selective UWA channel model into an expansion of complex exponential basis. Based on the basis expansion model (BEM) of UWA channels, we then equivalently transform the channel estimation problem into the recovery of sparse BEM coefficients. Finally, a modified sparse signal recovery scheme based on block sparse Bayesian learning is proposed to estimate the doubly selective UWA channel state information (CSI). Simulation results confirm its performance merits.

In this paper, a spectral efficient channel estimation scheme is proposed for spatial-correlated sparse massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with superimposed time-frequency training sequences (TSs) based on basis expansion model (BEM). This scheme first relies on the identical time-domain TS to acquire the partial channel common support, based on which the frequency-domain TSs inserted into the OFDM block are then utilized for accurate channel state information (CSI) acquisition. Besides, the superimposed TSs are adopted to reduce the TS overhead. Meanwhile, by exploiting the spatial correlations of massive MIMO channels, BEM can be utilized to reduce the number of the frequency-domain TSs and improve the spectral efficiency drastically from 22% to 95%. Simulation results show that the proposed scheme with 200 frequency-domain TSs can achieve almost the same performance as the conventional approach with 3200 frequency-domain TSs in massive MIMO systems with 256 transmit antennas.

In this study, a spectral efficient channel recovery scheme is proposed for spatial‐temporal correlated sparse massive multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing systems. By exploiting the spatial‐temporal correlations of massive MIMO channels, superimposed time‐frequency training sequences (TSs) are jointly utilised for accurate channel state information (CSI) acquisition. Moreover, a novel time‐frequency TS pattern design scheme is proposed to improve the recovery performance based on structured compressive sensing theory. Furthermore, the discrete prolate spheroidal basis expansion model of massive MIMO channels in the space‐domain is utilised to reduce the frequency‐domain pilots overhead and improve the spectral efficiency. Simulation results show that the proposed scheme could achieve accurate CSI with great improvement in spectral efficiency and computational complexity.

The concept of generalized spatial modulation (GenSM) aided millimeter wave (mm-wave) multiple-input multiple-output (MIMO) has recently attracted substantial research interest, as it benefits from the large bandwidth of mm-wave MIMO, while maintaining a reduced number of radio frequency (RF) chains. However, due to the lack of precoding design, the preceding GenSM-aided mm-wave MIMO schemes suffered from severe performance loss. Inspired by the reduced-RF-chain structure making implementation cost low while maintaining the benefit of GenSM, in this paper, we incorporate the technique of hybrid precoding into GenSM-aided mm-wave MIMO, where a hybrid digital and analog precoding regime is proposed to enhance the system's achievable rate. Moreover, a closed-form expression is proposed to provide an accurate approximation to the spectral efficiency (SE) achieved by the proposed scheme. The proposed expression is further simplified in the region of high signal-to-noise ratio (SNR). By exploiting the proposed SE expressions as low-complexity cost functions, two algorithms, i.e. the gradient ascent algorithm and the high-SNR approximation algorithm, are exploited to optimize the hybrid precoders in terms of SE maximization. Finally, numerical simulations are provided to substantiate the superior SE performance achieved by the proposed scheme over other GenSM-aided mm-wave MIMO schemes as well as the state-of-the-art mm-wave MIMO systems.

Massive spatial modulation aided multiple-input multiple-output (SM-MIMO) systems have recently been proposed as a novel combination of SM and of conventional massive MIMO, where the base station (BS) is equipped with a large number of antennas and simultaneously serves multiple user equipment (TIE) that employ SM for their uplink transmission. Since the massive SM-MIMO concept combines the benefits of both the SM and massive MIMO techniques, it has recently attracted substantial research interest. In this paper, we study the achievable uplink spectral efficiency (SE) of a multi-cell massive SM-MIMO system, and derive closed-form expressions to asymptotically lower-bound the SE yielded by two linear BS combining schemes, including maximum ratio combining and zero forcing combining, when a sufficiently large number of BS antennas are equipped. The derivation takes into account the impact of transmitter spatial correlations, imperfect channel estimations, user-specific power controls, and different pilot reuse factors. The proposed asymptotic bounds are shown to be tight, even when the scale of BS antennas is limited. The new SE results facilitate a system-level investigation of the optimal number of uplink transmit antennas (TAs) N with respect to SE maximization. Explicitly, we provide theoretical insights on the SE of massive SM-MIMO systems. Furthermore, we demonstrate that massive SM-MIMO systems are capable of outperforming the SE of conventional massive MIMOs relying on single-TA TIEs.

The idea of combining spatial modulation (SM) with massive multiple-input multiple-output (MIMO) system, i.e. massive SM-MIMO, has recently attracted plenty of academic interest, as it could simultaneously harness the advantages of SM and massive MIMO. However, the spectral efficiency (SE) analysis for massive SM-MIMO is still lack of exploring. To this end, a novel SE expression is derived in this paper to provide a tight lower bound for massive SM-MIMO systems, in which the impacts of imperfect channel estimation and random user locations are all accounted for. The new SE expression enables an efficient system-level analysis, via which we can investigate the optimal number of user antennas for SE maximization. Interestingly, we found that massive SM-MIMO maintains a higher SE than the conventional massive MIMO with single-antenna users when a limited number of users are scheduled, while conventional massive MIMO is more suitable when a large number of users are served.

In conventional generalised spatial modulation (GSM) systems, the antenna combination selection is based on the distribution of channels, without channel state information (CSI). In this paper, an A-Star based heuristic method and a sequential forward selection method for antenna combination selection in GSM systems are proposed to provide a superior symbol error rate (SER) performance with CSI. The A-Star based heuristic method is designed to minimize the union bound of SER rather than to maximize the minimum Euclidean distance between symbols. And the sequential forward antenna combination selection method is designed to reduce the computational complexity compared to the A-Star based heuristic method, but with slight loss in SER performance. Monte Carlo simulations are implemented to verify the performance of these proposed methods.

Generalised spatial modulation (GSM) is extended from the concept of spatial modulation (SM). Due to its superior energy efficiency to classical multiple-input multiple-output (MIMO) techniques, GSM has attracted plenty of interest under the study of large-scale MIMO systems. In this paper, we propose a low-complexity symbol detector for GSM based on the framework of sparse Bayesian learning (SBL), which effectively recovers the GSM symbols by exploiting the inherent sparsity property of GSM. Compared to other sparsity-based symbol detectors, e.g., detectors based on the basis pursuit (BP) method and the orthogonal matching pursuit (OMP), the SBL-based detector is capable of achieving a superior reconstruction accuracy using less receive antennas. Numerical simulations are performed to substantiate the performance of the proposed detector.

Spatial modulation (SM) is a modulation scheme that exploits the spatial dimension of multiple-input multiple-output (MIMO) channels to encode the transmitted information. It is shown in this paper that, by properly rotating the phase of the transmitted symbols, the minimum Euclidean distance (MED) of the constellation of the received symbols can be enlarged and hence the error performance can be improved. Moreover, we propose an algorithm that incorporates an iteration process to design the phase-only pre-scaling factors, which only rotate the phases of different transmitted symbols according to the instantaneous channel realization. Simulation results demonstrate that the proposed method is capable of outperforming the conventional SM scheme and other pre-scaling SM schemes.

Extended spatial modulation (ESM) is proposed as a novel spatial modulation scheme in this paper. Extended spatial modulation is a spatial modulation design where the number of active antennas can be various. The data rate could be improved as more symbols are available for transmitting and thus more information bits can be modulated during each time slot. The minimum distance of the transmitted symbols can also be further maximized as more symbols are available for optimizing selection which further improves the symbol error performance. Moreover, a near-optimal detection scheme with low complexity is also proposed in this paper. An approximated analytical expression for the symbol error probability (SEP) of the proposed detection scheme is derived along with the complexity analysis. Numerical simulations are proposed to demonstrate the performance of the proposed detection scheme and to validate the theoretical analysis.