Orthogonal time frequency space (OTFS) modulation has emerged as an alternative to orthogonal frequency division multiplexing (OFDM) in high mobility scenarios due to its excellent performance in time-varying channels. With proper signal detection, the OTFS system can effectively utilize time and frequency diversity. However, most existing detection algorithms rely on message passing (MP), so their performance degrades significantly when the channel exhibits weak sparsity. The purpose of this paper is to propose a signal detection algorithm with excellent performance even in rich-scattering environments. We first model the OTFS signal detection problem as an optimization problem and an iterative shrinkage thresholding algorithm (ISTA)-based signal detection method is then proposed to solve the problem with better performance than the MP algorithm. Besides, we propose a modified fast iterative shrinkage-thresholding algorithm (FISTA)-based detection method by adopting an acceleration term to speed up iterative convergence. Numerical results show that the proposed algorithms have excellent convergence and BER performance.

The integrated sensing and communication system (ISAC) plays an important role in addressing spectrum scarcity and improving sensing and communication performance, laying the foundation for the development of 6G wireless networks. Due to the crucial importance of beam pattern for detection probability and sensing accuracy, this paper proposes a waveform design scheme of ISAC MIMO-OFDM signal to cope with complex environments in the multi-user and multi-target scenarios. We first formulate an optimization problem to maximize radar detection probability under the constraints of the similarity of beam pattern gain between different targets, peak average power ratio (PAPR) and communication user sum-rate. Then, the non-convex optimization problem can be solved by the successive convex approximation (SCA) method. Finally, numerical simulations show that the proposed waveform design scheme improves the radar detection probability and ensures that each target is detected fairly.

Orthogonal Time Frequency Space (OTFS) modulation has been proposed to provide seamless and reliable communication in high-mobility environments. In particular, OTFS modulation enables the delay-Doppler (DD) domain model to be represented sparsely so that the compressed sensing (CS) algorithms can be applied in channel estimation to get more accurate channel state information (CSI). However, conventional CS algorithms in OTFS channel estimation assume that channel sparsity$K$is known, which is not available in some practical applications. In this paper, we propose the sparsity adaptive matching pursuit (SAMP) algorithm for OTFS channel estimation without prior information of the channel sparsity$K$, and we further propose a variable step-size backtracking sparsity adaptive matching pursuit (VSB-SAMP) to improve both accuracy and reconstruction speed. We first formulate the channel estimation problem as a sparse signal recovery problem. Then, the SAMP algorithm is introduced to solve the problem that channel sparsity is unknown. Furthermore, a VSB-SAMP is proposed to accelerate reconstruction speed. Simulation results show that the proposed algorithm can achieve accurate channel state information with less iterations.

Aircraft-assisted wireless communications have been extensively used in the military and civil fields. With the increasing applications for high data rate aircraft-assisted services such as virtual reality, ultra-high definition transmission video, there is an explosive demand for data rate in high mobility air-to-air (A2A) communication. There exist some promising solutions for high data rate wireless communications, including millimeter wave (mmWave) communications and massive MIMO hybrid precoding. However, most of previous works consider time invariant channel, and assume perfect channel state information (CSI), which is unsatisfactory in high mobility system. And a novel hybrid precoding strategy under high mobility scenarios is designed in this paper. First, some preliminaries about mmWave massive MIMO fast time-varying channel model is given. Then location-aided analog precoder and combiner are designed, and time-varying digital precoder based on matrix series approximation is also proposed. Moreover, the performance loss is analyzed through average effective channel power loss. Simulation results manifest that compared with the conventional strategy, the proposed strategy effectively improves the spectral efficiency in high mobility scenarios.

Phase noise (PN) introduced by the oscillator at the base station and user side severely degrades the channel estimation performance. This paper investigates the impact of PN on downlink compressive channel estimation in massive multiple-input multiple-output (MIMO) systems. Particularly, the downlink compressive channel estimation with PN is modeled as a sparse signal recovery problem with additive correlated perturbation on the pilot matrix, which is a general formulation for both non-synchronous and synchronous PN. Based on this signal model, the performance of the equivalent sensing matrix is analyzed by invoking restricted isometry property (RIP) in compressive sensing. In addition, the upper bound for $l_{1}$ -minimization based channel estimation method and tight channel estimation bound are derived in the framework of RIP and Oracle least square methodology, respectively. Finally, we propose a PN-aware sparse Bayesian learning (PNA-SBL) algorithm to improve the channel estimation performance in the presence of synchronous PN. Simulation results demonstrate our analysis and superiority of the proposed PNA-SBL algorithm.

Channel state information (CSI) is essential to achieve the optimal configuration of hybrid precoders and combiners in millimeter wave (mmWave) communication system. Exploiting the sparsity of mmWave channel enables to improve the CSI quality with small training overhead. In this paper, to further reduce the training overhead for channel estimation, the joint sparsity of wideband mmWave channel in angular-delay domain is exploited, where the mmWave channel estimation is formulated as a block sparse recovery problem. Accordingly, the block coherence of equivalent sensing matrix is smaller than the coherence of original sensing matrix, and decreases with the length of channel delay taps in the lower bound. The lower block coherence in turn elevates the recovery probability of unknown sparse channel. Finally, we proposed to employ the block orthogonal matching pursuit to exploit the derived block sparsity of wideband mmWave channel. The simulation results verify the analysis and demonstrate that the proposed block sparse recovery scheme outperforms the existing wideband mmWave channel estimators in terms of both the estimation accuracy and required training overhead.

In this paper, we investigate the physical layer security of an underlay cognitive decode-and- forward (DF) relay network, in which one secondary transmitter (ST) exchanges confidential information with one secondary destination (SD) with the aid of multiple cognitive DF relays by sharing the licensed spectrum of one primary user (PU), and multiple colluding eavesdroppers attempt to intercept the secondary transmission. Due to deep fading, it is assumed that there is no ST-SD link, one of the cognitive relays is selected to convey the confidential information, while the others emit artificial noise (AN) in an effort to confuse the eavesdroppers. In the absence of the eavesdroppers' channel information state (CSI), we analyze the secrecy outage performance under two opportunistic relay selection schemes, which are the conventional relay selection (CRS) and the best relay selection (BRS) schemes. We derive novel closed-form expressions for the secrecy outage probability with these two relay selection schemes. Simulation results are provided to verify our analysis, and shows that the secrecy performance of the propose BRS scheme significantly outperforms the CRS one.

Compared with ordinary linear antenna arrays or planar antenna arrays, space conformal antenna array has many advantages, such as the flexibility and accuracy of the beam. However, conventional algorithms cannot be applied to the designing of conformal antenna arrays, so the weighted minimum mean square error algorithm is proposed. In this paper, a new iterative phase correction algorithm is presented to improve the weighted minimum mean square error (MMSE) algorithm. By using the new algorithm, the shape of the beam can be controlled more flexibly, and more interferences can be resisted compared with the weighted minimum mean square error algorithm. The simulation results show an improved performance in beamforming.

Acquisition of accurate channel state information (CSI) at transmitters results in a huge pilot overhead in massive multiple input multiple output (MIMO) systems due to the large number of antennas in the base station (BS). To reduce the overwhelming pilot overhead in such systems, a structured joint channel estimation scheme employing compressed sensing (CS) theory is proposed. Specifically, the channel sparsity in the angular domain due to the practical scattering environment is analyzed, where common sparsity and individual sparsity structures among geographically neighboring users exist in multi-user massive MIMO systems. Then, by equipping each user with multiple antennas, the pilot overhead can be alleviated in the framework of CS and the channel estimation quality can be improved. Moreover, a structured joint matching pursuit (SJMP) algorithm at the BS is proposed to jointly estimate the channel of users with reduced pilot overhead. Furthermore, the probability upper bound of common support recovery and the upper bound of channel estimation quality using the proposed SJMP algorithm are derived. Simulation results demonstrate that the proposed SJMP algorithm can achieve a higher system performance than those of existing algorithms in terms of pilot overhead and achievable rate.

An exact closed-form average bit-error rate expression of a selection combining (SC) scheme is derived for a differential cooperative system employing an amplify-and-forward relay with simultaneous wireless information and power transfer capability. Monte Carlo simulations verify the analytical expressions. In particular, the SC scheme can achieve the same performance as that of a suboptimal maximal ratio combining scheme, and this is achieved with lower implementation complexity and energy consumption for SC.

Two weighted selection combining (WSC) schemes are proposed for a differential decode-and-forward relaying system in Rayleigh fading channels. Compared to the conventional SC scheme, the decision variable of the relay link is multiplied by a scale factor to combat the error propagation phenomenon. Average bit-error rate (ABER) expressions of the two proposed WSC schemes are derived in closed form and verified by simulation results. For the second WSC scheme, asymptotic ABER expression and diversity order are derived to gain more insight into this scheme. Moreover, it is demonstrated that both WSC schemes can overcome the extra noise amplification induced by the link adaptive relaying scheme. The first WSC scheme is slightly inferior to the second one, which has a higher complexity. Both proposed WSC schemes outperform the conventional SC scheme.

The decisions of routing and scheduling in typical backpressure framework are actually made jointly, which hurts modularity and imposes difficulties in practice. In addition, the backpressure style routing and scheduling usually lead poor delay performance. This paper proposes a throughput utility maximization algorithm for multisession multihop wireless networks by applying the Lyapunov optimization technique. The algorithm realizes the separation of routing and scheduling as well as incorporates delay coefficients in the transmission scheduling to reduce average end- to-end delay. It is proved through rigorous theoretical analyses and verified by simulation that the time average throughput utility of the algorithm can be arbitrarily close to the optimal utility with network stability. The benefits of the scheme are further investigated through simulation in Matlab.

To improve physical (PHY) layer security of a wireless relay system in the presence of an eavesdropper, a two-phase cooperative relaying scheme is investigated in this paper. In phase I, the source transmits confidential message, simultaneously, it cooperates with the friendly jammers and destination to create jamming signal at the eavesdropper without affecting the forwarding relay which is preselected. In phase II, the forwarding relay retransmits the decoded signal, meanwhile, the particular relay cooperates with the friendly jammers to create jamming signal at the eavesdropper without affecting the destination. We focus on the investigation of optimal power allocation for maximizing achievable secrecy rate subject to a total power constraint. Optimal relay selection and suboptimal relay selection schemes are also proposed. It is shown that as the number of relays increases, both secrecy rate and the performance of suboptimal relay selection scheme improve significantly. Numerical results are presented to validate the derived analytical results and compare them to existing work.

In this paper, we consider constellation design for decode-and-forward relay networks with adaptive modulation, which improves the diversity combining gain by averaging over all transmission branches the bit reliability variations brought by multilevel modulation formats. A novel bit-error-rate expression for maximal ratio combining of M-ary Quadrature Amplitude Modulation (M-QAM) signals with different modulation levels is derived based on log-likelihood ratios and employed as the metric to optimize the respective constellation schemes for source and relay nodes. A heuristic algorithm with very low computational complexity is proposed as a suboptimal solution to this problem and its concept is demonstrated by analyzing the combining process of 8QAM and 64QAM signals. Computer simulations under AWGN and Rayleigh fading channels show that our proposed constellation scheme improves the diversity combining gain effectively at the expense of only a marginal implementation complexity increase.