This paper investigates a multi-satellite cooperative covert satellite communication (SatCom) scheme, where a positive covert rate is achieved in ultra-dense low Earth orbit (LEO) satellite constellations by exploiting both interference and directional uncertainty. Specifically, interference arises from aggregate sidelobe leakage from other satellite transmissions, while directional uncertainty stems from the random selection of the transmitting satellite among multiple accessible ones. To this end, we first propose a LEO satellite network model and formulate the corresponding hypothesis testing problem for the cooperative covert SatCom scheme. The power distribution of the aggregate interference is quantified and approximated using stochastic geometry. Next, by analyzing the detection error probability and outage probability under the impact of aggregate interference, we derive an approximate covert capacity expression for the case of a single accessible satellite, which maintains a positive covert rate even as the slot length approaches infinity. Furthermore, by leveraging directional uncertainty through hiding the signal’s angle of arrival, we analyze the multi-satellite cooperative covert SatCom scheme, leading to a concise approximate expression that reveals significant covert capacity improvement. Numerical simulations are performed to verify the superiority of the proposed scheme, suggesting that a positive covert capacity can be achieved with interference uncertainty and significantly enhanced by directional uncertainty as the number of satellites increases.

This paper investigates covert communication in ultra-dense low Earth orbit (LEO) satellite systems, where the uncertainty of the aggregated interference formed by sidelobe leakages of satellite transmissions can be exploited to hide wireless signals from being detected by a warden. Specifically, we first propose a LEO satellite network model and the corresponding hypothesis testing problem for the covert satellite communication scheme, where the power distribution of the aggregated interference is quantified and then approximated by using stochastic geometry. After that, to analyze the impact of the aggregated interference, the covert transmission problem under constraints of the detection probability at Willie and the outage probability at Bob is formulated, where the achievable covert capacity is analyzed by considering different number of satellites N. Finally, numerical simulations are performed to verify the derived analytical results, which demonstrate that the covert capacity initially increases as$N$increases, while it becomes approximately proportional to$1/\sqrt{N}$for sufficiently large N.

In non-cooperative communications, blind recognition of channel codes is the key to recover information from noisy intercepted messages. In this paper, we develop a progressive and iterative approach to reconstruct the parity-check matrices of large QC-LDPC codes under high bit error rate (BER). Specifically, in order to reduce the impact of noisy bits and improve efficiency for large codes, a novel sparse vector recovery (SVR) method based on sub-matrix sampling is first introduced. Then, SVR is operated iteratively after employing intermediate decoding with the partially reconstructed parity-check matrix on the intercepted messages. At last, the full parity-check matrix is built up progressively. Experimental results on a code of length 16200 show that the maximum bearable BER of the proposed approach to fully reconstruct is around 3E-4, at least 100 times higher than previous works.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multiuser near-field wideband communication system is investigated, in which a robust deep reinforcement learning (DRL) based algorithm is proposed to enhance the users’ achievable rate by jointly optimizing the active beamforming at the base station (BS) and passive beamforming at the STAR-RIS. To mitigate the beam split issue, the delay-phase hybrid precoding structure is introduced to facilitate wideband beamforming. Considering the coupled nature of the STAR-RIS phase-shift model, the passive beamforming design is formulated as a problem of hybrid continuous and discrete phase-shift control, and the proposed algorithm controls the high-dimensional continuous action through hybrid action mapping. Additionally, to address the issue of biased estimation encountered by existing DRL algorithms, a softmax operator is introduced into the algorithm to mitigate this bias. Simulation results illustrate that the proposed algorithm outperforms existing algorithms and overcomes the issues of overestimation and underestimation.

Considering the charging needs of the Internet of Things, we introduce the simultaneous wireless information and power transfer (SWIPT) technology into the ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul. However, SWIPT can bring potential eavesdropping issues. In this paper, we study the secure communication caused by SWIPT in the UD-CRAN network. Specifically, the transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense networks, such as base station diversity and high probability of line-of-sight transmission. Aiming at maximizing the security energy efficiency, we jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of remote radio heads (RRHs), and user-RRH association. We propose an iterative algorithm based on the Dinkelbach’s transform to deal with the fractional objective function. To solve the mix-integer non-convex inner problem, we design: (1) a successive convex approximation (SCA) based method in which the problem at each iteration is a mixed-integer second-order cone program; (2) and an alternating optimization algorithm based on semidefinite relaxation (SDR) to further balance the complexity and performance. Finally, numerical results are presented to demonstrate the efficiency of the proposed schemes. Moreover, the proposed SCA method can achieve excellent performance while preserving integer variables, which inevitably increases algorithm complexity. Furthermore, the proposed SDR method can avoid the iteration process of SCA and further reducing the algorithm complexity.

This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.

Abstract We present an overview of quantum-safe cryptography (QSC) with a focus on post-quantum cryptography (PQC) and information-theoretic security. From a cryptographic point of view, lattice and code-based schemes are among the most promising PQC solutions. Both approaches are based on the hardness of decoding problems of linear codes with different metrics. From an information-theoretic point of view, lattices and linear codes can be constructed to achieve certain secrecy quantities for wiretap channels as is intrinsically classical- and quantum-safe. Historically, coding theory and cryptography are intimately connected since Shannon’s pioneering studies but have somehow diverged later. QSC offers an opportunity to rebuild the synergy of the two areas, hopefully leading to further development beyond the NIST PQC standardization process. In this paper, we provide a survey of lattice and code designs that are believed to be quantum-safe in the area of cryptography or coding theory. The interplay and similarities between the two areas are discussed. We also conclude our understandings and prospects of future research after NIST PQC standardisation.

This paper studies the non-orthogonal multiple access (NOMA)-based spatial modulation (SM) systems with multiple subchannels. A mixed multicast and unicast transmission is considered in each channel, in which a common content is multicasted in the transmit antenna (TA) domain to all the users, and the unicast contents are transmitted as amplitude- phase modulated (APM) symbols in the classical signal domain using NOMA via the active antenna. First, we obtain the achievable unicast rate for each user and an upper bound for the achievable multicast rate in the TA domain. Then, the subchannel assignment and power allocation schemes are designed to maximize the system sum rate. Specifically, the subchannel assignment is formulated as a many-to-one matching with peer effect, and we propose a suboptimal but efficient algorithm incorporating the swap operation to solve it. We then optimize the power allocation subproblem by employing the successive convex approximation approach, which iteratively approximates the original nonconvex problem to a convex one. Finally, numerical results are presented to demonstrate the effectiveness of the proposed schemes.

This paper studies the multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio (SINR) requirement for each user, the sum power constraint at each gateway as well as the per feed power constraints at the satellite. We propose two algorithms to solve the formulated problem. In the first algorithm, by employing the successive convex approximation (SCA) approach, we iteratively approximate the original nonconvex problem to a second-order cone program (SOCP) which can be solved by modern solvers efficiently. For the second, we propose a modified joint power control and beamforming algorithm which computes QoS beamforming and geometric programming (GP) based power allocation iteratively. Compared to the traditional method [5] which uses a subgradient and projection based approach for the power control, the GP based solution is more implementation friendly and practical appealing which also achieves a slightly better sum rate performance. Finally, numerical results are presented to validate the efficiency of the proposed schemes.

Pricing is an effective mechanism for network resource allocation that can be used to achieve a desirable balance between efficiency and fairness. However, sophisticated utility models are needed to guarantee the performance of price-based resource allocation, especially with regard to video transmission. Among various performance indices of video transmission, rebuffering is an important one that influences user quality of experience (QoE). Therefore, a price-based bandwidth allocation scheme for a dynamic adaptive streaming over hypertext transfer protocol (DASH) system is proposed for mitigating the effect of rebuffering on QoE. The utility model of the proposed scheme considers the relationship between the allocated bandwidth and the rebuffering length, as well as the effect of rebuffering length on QoE. Specifically, electroencephalography (EEG) experiments are conducted, and the distribution of the subjects' time limits at which rebuffering arouse negative emotions is used for calibration. Based on this model, the DASH server collects the buffer state information of all DASH clients periodically to adjust its bandwidth allocation. Assuming the server protects itself from congestion by pricing the clients' requested bandwidth, a Stackelberg game is formulated to study the joint utility maximization on the revenue of the server and the utility of the clients. The Stackelberg equilibrium of the game is characterized, and an efficient searching algorithm is proposed for its solution. EEG experiments are repeated on another group of subjects to verify the generalization ability of the results, and simulation results are presented to validate the effectiveness of the proposed algorithm. The proposed algorithm is shown to have low complexity and outperforms both traditional price-based scheme and QoE maximized scheme in terms of rebuffering.

As more mobile users become subscribers of content services, their subscribed content can be directly pushed from the content provider into the user equipment after the content is generated. In current and future network paradigms, a joint wired and wireless transmission design for this pushing is needed to guarantee the user experience without the extra deployment of communication infrastructures or consumption of resources. In this paper, we investigate a joint wired and wireless content delivery system that incorporates wired and wireless multicast. The users in the same group are served by wireless multicast from a base station (BS), while the BSs of the same content form a multicast tree in a backbone wired network. The sum of wired and wireless traffic is minimized by a joint design of user grouping, subchannel allocation, wired routing, and wired link usage. Exploiting the monotonicity of wired and wireless traffic with regard to the wired hop count, the original problem is converted for searching the optimal hop count vector that achieves the minimum sum of both types of traffic, which is solved by a monotonic optimization (MO)-based iterative algorithm. Compared with existing schemes and according to the numerical results, a reduction in total traffic of 43% can be achieved by our approach.

Pushing contents from content providers (CPs) directly to user equipments (UEs) during off-peak hours can significantly reduce the incurring traffic during peak hours. Both wired and wireless transmission costs are considerable in this scenario, especially in current cellular networks where the backhaul is regarded as a bottleneck of transmission. In order to alleviate the traffic pressure over the network without loss of users' quality of experience, this paper presents a joint wired and wireless transmission scheme which considers grouping, subchannel allocation, wired routing, and wired bandwidth allocation. An iterative algorithm is proposed to achieve the tradeoff between those two kinds of traffic. The users in the same group are served by a single multicast transmission from a base station (BS), while the BSs which multicast the same content form a multicast tree in backbone wired network. Compared with traditional unicast scheme, our approach can reduce the wired, wireless, and total traffic by 23%, 46%, and 34% at most according to the simulation results.

In this paper, the joint beamforming design for max-min fair simultaneous wireless information and power transfer (SWIPT) is investigated in a green cloud radio access network (Cloud-RAN) with millimeter wave (mmWave) wireless fronthaul. To achieve a balanced user experience for separately located data receivers (DRs) and energy receivers (ERs) in the network, joint transmit beamforming vectors will be optimized to maximize the minimum data rate among all the DRs, while satisfying each ER with sufficient RF energy at the same time. Then, a two-step iterative algorithm is proposed to solve the original non- convex optimization problem with the fronthaul capacity constraint in an l0-norm form. Specifically, the l0-norm constraint can be approximated by the reweighted l1-norm, from which the optimal max-min data rate and the corresponding joint beamforming vector can be derived via semidefinite relaxation (SDR) and bi-section search. Finally, extensive numerical simulations are performed to verify the superiority of the proposed joint beamforming design to other separate beamforming strategies.

Long Term Evolution (LTE) deployment in the unlicensed spectrum is considered a promising solution to overcome spectrum shortage. To ensure fair coexistence among unlicensed users, two Load Based Equipment (LBE) access technologies are introduced in European Telecommunications Standards Institute (ETSI) standard. However, it is not clear whether the two LBE protocols can ensure fair channel access between unlicensed Licensed Assisted Access (LAA) and Wi-Fi users. To this end, renewal theory based analytical models are developed to study the performance of these two LBE random access protocols. Specifically, the throughput performance of Wi-Fi users and LAA users is first derived and compared. Our results show that both options may not achieve throughput fairness among Wi-Fi and LAA users if the key protocol parameters are not fine tuned. Generally option A favors Wi-Fi users, while option B favors LAA users. To improve the fairness performance, channel access parameters in both protocols should be adapted to network conditions in order to achieve the best coexisting performance in terms of both fairness and network throughput. The analysis provides important guidance for the implementation of Listen-before-Talk based access mechanisms in LAA. Extensive simulations using NS-3 are conducted to validate the accuracy of the models.

In this paper, we consider a green cloud radio access network (C-RAN) with simultaneous wireless and power transfer ability. In order to reduce the energy consumed for updating the channel state information (CSI), energy users are divided into two different groups, including the free charge group and the MIMO group. Then a semi-definite programming problem is formulated under the constraints of energy and information transmission requirements. To minimize the total energy consumption, two algorithms are developed to authorize the energy users into two group divisions in single time slot. Then the algorithms are extended to long term scenarios consisting training and long term stages, the CSI of free charge energy users are not required during the long term stage. Simulation and numerical results are presented to demonstrate the efficiency of the proposed algorithms in significantly reducing the energy consumption of C-RAN systems.

In this paper, we investigate joint beamforming for energy-throughput tradeoff in a sustainable cloud radio access network system, where multiple base stations (BSs) powered by independent renewable energy sources will collaboratively transmit wireless information and energy to the data receiver and the energy receiver simultaneously. In order to obtain the optimal joint beamforming design over a finite time horizon, we formulate an optimization problem to maximize the throughput of the data receiver while guaranteeing sufficient RF charged energy of the energy receiver. Although such problem is non-convex, it can be relaxed into a convex form and upper bounded by the optimal value of the relaxed problem. We further prove tightness of the upper bound by showing the optimal solution to the relaxed problem is rank one. Motivated by the optimal solution, an efficient online algorithm is also proposed for practical implementation. Finally, extensive simulations are performed to verify the superiority of the proposed joint beamforming strategy to other beamforming designs.

In this paper, we consider a fully sustainable cooperative communication system which consists of multiple source nodes with radio-frequency (RF) energy harvesting capabilities, a half-duplex relay node with renewable energy supplies, and a destination node. Specifically, the relay node is powered by the green energy harvested from renewable sources such as solar or wind, while the source nodes are wirelessly charged by the RF energy from the relay node's forwarding signals to the destination node. An optimal joint time scheduling and power allocation problem is formulated to achieve the maximum system sum-throughput of the users over a finite time horizon. To tackle the formulated NP-hard non-convex mixed integer nonlinear programming problem, we first analyze its upper bound by problem reformulation and relaxation, which can be simplified by the directional water filling algorithm and iteratively solved by sequential parametric convex approximation. We then propose an optimal branch-and-bound framework to solve the formulated problem, and develop an efficient sub-optimal offline algorithm and a heuristic online algorithm to reduce the computational complexity. Finally, extensive simulations are conducted to verify the superiority of the proposed solution and demonstrate that the sub-optimal algorithm approaches the performance upper bound with polynomial time complexity.

This paper proposes a novel approach, based on unequal error protection, to enhance rateless codes with progressive recovery for layered multimedia delivery. With a parallel encoding structure, the proposed Progressive Rateless codes (PRC) assign unequal redundancy to each layer in accordance with their importance. Each output symbol contains information from all layers, and thus the stream layers can be recovered progressively at the expected received ratios of output symbols. Furthermore, the dependency between layers is naturally considered. The performance of the PRC is evaluated and compared with some related UEP approaches. Results show that our PRC approach provides better recovery performance with lower overhead both theoretically and numerically.