A DRL-based resource allocation for IRS-enhanced semantic spectrum sharing networks

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

Spectrum sensing (SS) is a key enabling function in cognitive radio Internet of Things (CR-IoT) networks, facilitating dynamic spectrum access by identifying available frequency bands without causing interference to licensed primary users (PUs). However, in practical wireless environments, severe fading and path loss significantly attenuate PU signals, leading to unreliable detection at low signal-to-noise ratios (SNRs). Conventional energy detection (ED) schemes often fail to achieve the regulatory detection probability requirement (Pd ≥ 0.9) when SNR < −15 dB, resulting in inefficient spectrum utilization. To overcome this limitation, this paper proposes an intelligent reflecting surface (IRS)-assisted spectrum sensing framework for CR-IoT networks. The IRS employs M passive reflective elements to enhance the effective channel gain between the PU and secondary user (SU) through optimized phase shift configuration, thereby improving the received signal power at the SU. The proposed technique adaptively adjusts the IRS phase shifts to maximize PU signal reflection without additional power consumption. Simulated results demonstrate that suggested method achieves up to 60% higher Pd and significantly reduces the false alarm (Pf) compared to conventional and adaptive ED techniques, particularly under low SNR conditions and short sensing durations (e.g., 4 ms), highlighting its potential for next-generation wireless networks.

Multi-unmanned aerial vehicle (UAV)-assisted communication is a critical technology for the low-altitude economy, supporting applications from logistics to emergency response. Semantic communication effectively enhances transmission efficiency and improves the communication performance of multi-UAV-assisted systems. Existing research on multi-UAV semantic communication networks predominantly assumes static ground devices, overlooking computation offloading and resource allocation challenges when ground devices are mobile. This overlooks the critical challenge of dynamically managing computation offloading and resources for mobile users, whose varying channel conditions and semantic compression needs directly impact system performance. To address this gap, this paper proposes a multi-UAV-assisted semantic communication model that novelly integrates user mobility with adaptive semantic compression, formulating a joint optimization problem for computation offloading and resource allocation. The objective is to minimize the maximum task processing latency through the joint optimization of UAV–device association, UAV trajectories, transmission power, task offloading ratios, and semantic compression depth. To solve this problem, we design a MAPPO-APSO algorithm integrating alternating iteration, multi-agent proximal policy optimization (MAPPO), and adaptive particle swarm optimization (APSO). Simulation results demonstrate that the proposed algorithm reduces the maximum task latency and system energy consumption by up to 20.7% and 16.1%, respectively, while maintaining transmission performance and outperforming benchmark approaches.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

We study the throughput and delay scaling laws of cognitive radio networks (CRN) with mobile primary and secondary users. They operate at the same time, space and share the spectrum. The primary users (PUs) have higher priority to access the spectrum while the secondary users (SUs) should access opportunistically. Furthermore, we consider a unique situation where both PUs and SUs move according to a General Heterogeneous Speed-restricted Mobility (GHSM) model. In this model, we define (h+1) heterogeneous moving patterns using a universal set T={T_i |0&#8804;i&#8804;h,A_i=n^(-i &#967;_0/h)}, where A_i determines the moving area of each pattern. The set of primary (secondary) moving patterns T^((p)) (T^((s) )) is a randomly and independently selected subset of T. We assign n (n^&#946;, where &#946;&#62;1) primary (secondary) nodes to each moving pattern, and their initial positions are subject to a poisson point process. In addition, we have |T^((p) ) |&#126;|T^((s) ) |=&#952;(h)= &#952;(logn). By proposing a cooperative routing strategy, we fully utilize the mobility heterogeneity of primary and secondary users and achieve near-optimal throughput and delay performance of order &#952;(poly log n) when &#967;_0&#8805;&#946;. In other cases, our transmission scheme shows advantages over [1] in delay performance of the primary network (PN) and over [2] in capacity of the secondary network (SN).

Dynamic spectrum access has been proposed to address the spectrum scarcity. Based on this access mode, secondary users are allowed to opportunistically operate when primary users are absent. Secondary Users are, however, required to relinquish the spectrum upon return of the incumbent spectrum holder. Although it improves spectrum utility, opportunistic spectrum access limits secondary users' access to exogenous spectrum holes vacated by primary users. We argue that spectrum holes should be endogenous, enabled by incentives to incumbent users to increase spectrum availability, while mitigating risk. To this end, we take a different perspective on dynamic spectrum sharing and propose a coopetive framework, whereby primary and secondary users engage in a cooperative, yet competitive, auction-driven spectrum sharing to enhance spectrum usage. In this framework, the primary users'spectrum is organized in three bands, namely exclusive usage, usage right and management right bands. Based on this structure, spectrum is traded as financial options to increase trading flexibility and capture the value primary and secondary users attach to spectrum access and usage. Spectrum trading between primary and secondary users is formalized as a utility maximization problem. Approximate solutions to this problem are derived and their performance is analyzed. The results show that spectrum trading, which combines revocable and exclusive leasing, provides higher utilities for both primary and secondary users.

In this paper, we study resource allocation for secondary users (SUs) in underlay full-duplex cognitive networks, where the channel state information of the links between SUs and primary users (PUs) is uncertain. To protect the transmission of the PUs from interference generated by the SUs, we utilize robust optimization theory to characterize the channel uncertainty and formulate a resource allocation problem by jointly optimizing sub-channel assignment, user pairing, and power allocation. By using the dual method, we decompose the original resource allocation problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results demonstrate the effectiveness of our proposed algorithm.

In order to elevate the utility of licensed spectrum bands credit to coexistence within the same network of licensed or primary users and cognitive or secondary users, it is intended to have cognitive radio (CR) theory. Effective resource allocation between secondary and primary users is the most significant key aspect in this environment. In Cognitive Radio Networks (CRNs), resource allocation is used for total utilization of frequency spectrum. Limitations such as transmission power, interference threshold of primary users and traffic demands of secondary users builds the challenge for maximizing the energy-efficiency. In order to overcome the problem of maximum energy consumption and minimum energy efficiency, a Hybrid Water Filling (HWF) Algorithm is proposed. Combination of Multi-Objective Particle Swarm Optimization (MOPSO) and Iterative Water Filling (IWF) algorithm is baptized as HWF. MOPSO is used to optimize the sub channels and energy allocation to achieve the highest capacity under the total energy constraint. Thus by combining IWF with MOPSO, the energy efficiency is augmented and provides optimal energy transmission for resource allocation. The simulation results illustrate that the proposed algorithm could achieve the optimal energy allocation than the existing one in less amount of time.

We investigate opportunistic cooperation between secondary (unlicensed) users and primary (licensed) users in cognitive radio networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently with a primary user. However, it can employ cooperative relaying in order to improve the effective transmission rate of the primary user. In the second model, called the Interference Model, a secondary user is allowed to transmit its data concurrently with a primary user. However, the secondary user can “cooperate” by deferring its transmissions when the primary user is busy. In both models, the secondary users must make intelligent cooperation decisions as they seek to maximize their own throughput subject to average power constraints. The decision options are different during idle and busy periods of the primary user, and the decisions in turn influence the durations of these periods according to a controllable infinite state Markov chain. Such problems can be formulated as constrained Markov decision problems, and conventional solution techniques require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using a generalized Lyapunov optimization technique, we design a novel greedy and online control algorithm that overcomes these challenges. Remarkably, this algorithm does not require any knowledge of the network arrival rates and is provably optimal.

Intelligent reflecting surface (IRS) is a very promising technology for the development of beyond 5G or 6G wireless communications due to its low complexity, intelligence, and green energy-efficient properties. In this paper, we combined IRS with physical layer security (PLS) to solve the security issue of cognitive radio (CR) networks. Specifically, an IRS-assisted multi-input single-output (MISO) CR wiretap channel was studied. To maximize the secrecy rate of secondary users subject to a total power constraint (TPC) for the transmitter and interference power constraint (IPC) for a single antenna primary receiver (PR) in this channel, an alternating optimization (AO) algorithm is proposed to jointly optimize the transmit covariance R at transmitter and phase shift coefficient Q at IRS by fixing the other as constant. When Q is fixed, R is globally optimized by equivalently transforming the quasi-convex sub-problem to convex one. When R is fixed, bisection search in combination with minorization–maximization (MM) algorithm was applied to optimize Q from the non-convex fractional programming sub-problem. During each iteration of MM, another bisection search algorithm is proposed, which is able to find the global optimal closed-form solution of Q given the initial point from the previous iteration of MM. The convergence of the proposed algorithm is analyzed, and an extension of applying this algorithm to multi-antenna PR case is discussed. Simulations have shown that our proposed IRS-assisted design greatly enhances the secondary user’s secrecy rate compared to existing methods without IRS. Even when IPC is active, the secrecy rate returned by our algorithm increases with transmit power as if there is no IPC at all.

We consider a cooperation scenario between primary users and untrusted secondary users in cognitive radio networks. The secondary users are willing to help the primary users to relay the primary users' messages in reward for being allowed to share the primary users' spectrum bands. However, the primary users might be reluctant to accept this help, since the secondary users are untrustworthy and may try unauthorized decoding of the primary users' messages. This paper will answer the question: when is this cooperation mutually beneficial for primary and secondary users? Taking an approach of information-theoretic secrecy, such as coding techniques for wiretap channels, the primary users can allow the secondary users to sense and relay the message, while ensuring that the secondary users are ignorant of the primary users' messages. We characterize an achievable secrecy rate of primary users with a rate of the secondary users' communication. From the derived rate pairs, an optimization problem is formulated such that the secondary transmitter distributes its transmit power to maximize its data rate while providing a higher secrecy rate to the primary users. We demonstrate that this cooperation can provide a positive secrecy rate, even when a non-cooperative scheme achieves a zero secrecy rate.

Cognitive radio methodologies have the potential to dramatically increase the throughput of wireless systems. Herein, control strategies which enable the superposition in time and frequency of primary and secondary user transmissions are explored in contrast to more traditional sensing approaches which only allow the secondary user to transmit when the primary user is idle. In this paper, the optimal transmission policy for the secondary user when the primary user adopts a retransmission-based error control scheme is investigated. The policy aims to maximize the secondary users' throughput, with a constraint on the throughput loss and failure probability of the primary user. Due to the constraint, the optimal policy is randomized, and determines how often the secondary user transmits according to the retransmission state of the packet being served by the primary user. The resulting optimal strategy of the secondary user is proven to have a unique structure. In particular, the optimal throughput is achieved by the secondary user by concentrating its transmission, and thus its interference to the primary user, in the first transmissions of a primary user packet. The rather simple framework considered in this paper highlights two fundamental aspects of cognitive networks that have not been covered so far: 1) the networking mechanisms implemented by the primary users (error control by means of retransmissions in the considered model) react to secondary users' activity; 2) if networking mechanisms are considered, then their state must be taken into account when optimizing secondary users' strategy, i.e., a strategy based on a binary active/idle perception of the primary users' state is suboptimal.

In this paper, we present an analytical study for the performance of a two-tier primary-secondary network based on IEEE 802.11 DCF mechanism. It is assumed that multiple primary users (PUs) and secondary users (SUs) coexist in the radio environment and share a single band such that the SUs are allowed to contend only if they sense the channel idle for a certain period of time. First, we derive the PUs' medium access delay in the presence of SUs. Then, assuming an exponential packet inter-arrival time for the primary network, and using M/G/1 queue modeling, we determine the collision probability and throughput for the primary and secondary networks, as well as the PUs' average total delay including the queuing delay. Numerical along with simulation results show how the performance in the primary network, with given packet arrival rate, can be affected as functions of the sensing time, packet payload size and population size of the SUs. The findings of this work can be used effectively to study the performance of any IEEE 802.11 based primary-secondary network, such as cognitive radio networks where SUs may exploit a primary channel opportunistically based on a predefined policy, as well as to optimize the performance of distributed cognitive radio MAC protocols.

Power allocation for secondary users (SUs) in cognitive networks is an important issue to ensure the SUs’ quality of service. When the mutual interference between the primary users (PUs) and the SUs is taken into consideration, it is wanted to achieve the conflict-free power allocation while synchronously maximizing the capacity of the secondary network. In this paper, the optimal power allocation problem is considered in orthogonal frequency division multiplexing cognitive networks. The single SU case is primarily formulated as a constrained optimization problem. On this basis, the multiple SUs case is then studied and simulated in detail. During the analysis, the mutual interference among the PUs and the SUs is comprehensively formulated as the restrictions on the SU’s transmission power and the optimization problems are finally resolved by iterative water-filling algorithms. Consequently, the proposed power allocation scheme restrains the interference to the primary network, as well as maximizing the capacity of the secondary network. Specifying the multiple-SUs case, simulation results are exhibited in a simplified scenario to confirm the efficiency of the proposed water-filling algorithm, and the influence of the mutual interference on the power allocation and the system capacity is further illustrated.

A novel protocol for transparent and simultaneous spectrum access between the secondary user and the primary user in cognitive radio networks