Space-air-ground integrated networks (SAGINs) will play a pivotal role in 6G communication systems. They are considered a promising technology for enhancing network capacity in densely populated urban areas and extending connectivity to rural regions. However, the complex, multilayered, and heterogeneous nature of SAGINs demands an innovative approach to designing their multi-tier associations. In this context, we propose a modeling of the SAGINs association problem using multi-sided matching theory. Our objective is to devise a reliable, asynchronous, and fully distributed approach that associates nodes across the layers to maximize the total end-to-end rate of the assigned agents. To achieve this, our problem is formulated as a multi-sided many-to-one matching game. We introduce a randomized matching algorithm with minimal information exchange. The algorithm is shown to reach an efficient and stable association between nodes in adjacent layers. Simulation results show that our proposed approach yields significant gains compared to both greedy and distance-based algorithms,

This work considers the deployment of unmanned aerial vehicles (UAVs) over a predefined area to serve a number of ground users. Due to the heterogeneous nature of the network, the UAVs may cause severe interference to the transmissions of each other. Hence, a judicious design of the user-UAV association and UAV locations is desired. A potential game is defined where the players are the UAVs. The potential function is the total sum rate of the users. The agents' utility in the potential game is their marginal contribution to the global welfare or their so-called wonderful life utility. A game-theoretic learning algorithm, binary log-linear learning (BLLL), is then applied to the problem. Given the potential game structure, a consequence of our utility design, the stochastically stable states using BLLL are guaranteed to be the potential maximizers. Hence, we optimally solve the joint user-UAV association and 3-D-location problem. Next, we exploit the submodular features of the sum rate function for a given configuration of UAVs to design an efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it comes with a performance guarantee of 1-1/e of the optimal solution. To further reduce the number of iterations, we propose another heuristic greedy algorithm that provides very good results. Our simulations show that, in practice, the proposed greedy approaches achieve significant performance in a few iterations.

We consider a two-sided one-to-one abstract matching problem with a defined notion of pairwise stability. The formulated problem is shown to encompass the ordinal and cardinal utility markets. We propose a distributed blind matching algorithm (BLMA) to solve the problem. The BLMA is characterized by random activations of agents, and by generic negotiation and aspiration (utility) update processes. We prove that the solution produced by the BLMA will converge to an ϵ-pairwise stable, equivalently ϵ-core, outcome with probability one. We then consider three BLMA applications in cognitive radio networks. We propose a simple BLMA negotiation and aspiration update dynamic to produce an ϵ-pairwise stable solution for the case of quasi-convex and quasi-concave utilities. In the case of more general utility forms, we show another BLMA process to provide equilibrium. We also consider the use of the BLMA in an ordinal utility market. In all applications of the BLMA, we impose a limited information exchange in the network so that agents can only calculate their own utilities, but no information is available about the utilities of any other user in the network.

We consider a cognitive radio network where secondary users (SUs) are allowed access time to the spectrum belonging to the primary users (PUs) provided that they relay primary messages. PUs and SUs negotiate over allocations of the secondary power that will be used to relay PU data. We formulate the problem as a generalized assignment market to find an epsilon pairwise stable matching. We propose a distributed blind matching algorithm (BLMA) to produce the pairwise-stable matching plus the associated power allocations. We stipulate a limited information exchange in the network so that agents only calculate their own utilities but no information is available about the utilities of any other users in the network. We establish convergence to epsilon pairwise stable matchings in finite time. Finally we show that our algorithm exhibits a limited degradation in PU utility when compared with the Pareto optimal results attained using perfect information assumptions.

We consider a network comprised of multiple primary users (PUs) and multiple secondary users (SUs), where the SUs seek access to a set of orthogonal channels each occupied by one PU. Only one SU is allowed to coexist with a given PU. We propose a distributed matching algorithm to pair the network users, where a Stackelberg game model is assumed for the interaction between the paired PU and SU. The selected secondary is given access in exchange for monetary compensation to the primary. The PU optimizes the interference price it charges to a given SU and the power allocation to maintain communication. The SU optimizes its power demand so as to maximize its utility. Our algorithm provides a unique stable matching. Numerical results indicate the advantage of the proposed algorithm over other reference schemes.

We propose a three-message superposition coding scheme in a cognitive radio relay network exploiting active cooperation between primary and secondary users. The primary user is motivated to cooperate by substantial benefits it can reap from this access scenario. Specifically, the time resource is split into three transmission phases. The first two phases are dedicated to primary communication, while the third phase is for the secondary's transmission. We formulate two throughput maximization problems for the secondary network subject to primary user rate constraints and per-node power constraints with respect to the time durations of primary transmission and the transmit power of the primary and the secondary users. The first throughput maximization problem assumes a partial power constraint such that the secondary power dedicated to primary cooperation, i.e. for the first two communication phases, is fixed apriori. In the second throughput maximization problem, a total power constraint is assumed over the three phases of communication. The two problems are difficult to solve analytically when the relaying channel gains are strictly greater than each other and strictly greater than the direct link channel gain. However, mathematically tractable lowerbound and upperbound solutions can be attained for the two problems. For both problems, by only using the lowerbound solution, we demonstrate significant throughput gains for both the primary and the secondary users through this active cooperation scheme. We find that most of the throughput gains come from minimizing the second phase transmission time since the secondary nodes assist the primary communication during this phase. Finally, we demonstrate the superiority of our proposed scheme compared to a number of reference schemes that include best relay selection, dual-hop routing, and an interference channel model.

We propose a three-message superposition coding scheme in a cognitive radio relay network exploiting active cooperation between primary and secondary networks. The time resource is split into three transmission phases. The first two phases are dedicated to primary communication, while the third phase is for the secondary's transmission. We formulate a throughput maximization problem for the secondary network subject to primary user rate constraints and per-node power constraints with respect to the time durations of primary transmission and the power allocation at the primary and the secondary users. We provide an analytical solution to our problem and validate it by numerical optimization. We demonstrate significant throughput gains for both the primary and the secondary users through this active cooperation scheme. We find that most of the throughput gains come from minimizing the second phase transmission time. Finally, we demonstrate the superiority of our proposed scheme compared to best relay selection.

Sensing with equal gain combining (SEGC), a novel cooperative spectrum sensing technique for cognitive radio networks, is proposed. Cognitive radios simultaneously transmit their sensing results to the fusion center (FC) over multipath fading reporting channels. The cognitive radios estimate the phases of the reporting channels and use those estimates for coherent combining of the sensing results at the FC. A global decision is made at the FC by comparing the received signal with a threshold. We obtain the global detection probabilities and secondary throughput exactly through a moment generating function approach. We verify our solution via system simulation and demonstrate that the Chernoff bound and central limit theory approximation are not tight. The cases of hard sensing and soft sensing are considered and we provide examples in which hard sensing is advantageous to soft sensing. We contrast the performance of SEGC with maximum ratio combining of the sensors' results and provide examples where the former is superior. Furthermore, we evaluate the performance of SEGC against existing orthogonal reporting techniques such as time division multiple access (TDMA). SEGC performance always dominates that of TDMA in terms of secondary throughput. We also study the impact of phase and synchronization errors and demonstrate the robustness of the SEGC technique against such imperfections.

We characterize the achievable three-dimensional tradeoff between diversity, multiplexing, and delay of the single antenna Automatic Retransmission reQuest (ARQ) Z-interference channel. Non-cooperative and cooperative ARQ protocols are adopted under these assumptions. Considering no cooperation exists, we study the achievable tradeoff of the fixed-power split Han-Kobayashi (HK) approach. Interestingly, we demonstrate that if the second user transmits the common part only of its message in the event of its successful decoding and a decoding failure at the first user, communication is improved over that achieved by keeping or stopping the transmission of both the common and private messages. Under cooperation, two special cases of the HK are considered for static and dynamic decoders. The difference between the two decoders lies in the ability of the latter to dynamically choose which HK special-case decoding to apply. Cooperation is shown to dramatically increase the achievable first user diversity.

In the context of primary/secondary spectrum sharing, we propose a randomized secondary access strategy with access probabilities that are a function of both the primary automatic repeat request (ARQ) feedback and the spectrum sensing outcome. The primary terminal operates in a time slotted fashion and is active only when it has a packet to send. The primary receiver can send a positive acknowledgment (ACK) when the received packet is decoded correctly. Lack of ARQ feedback is interpreted as erroneous reception or inactivity. We call this the explicit ACK scheme. The primary receiver may also send a negative acknowledgment (NACK) when the packet is received in error. Lack of ARQ feedback is interpreted as an ACK or no-transmission. This is called the explicit NACK scheme. Under both schemes, when the primary feedback is interpreted as a NACK, the secondary user assumes that there will be retransmission in the next slot and accesses the channel with a certain probability. When the primary feedback is interpreted as an ACK, the secondary user accesses the channel with either one of two probabilities based on the sensing outcome. Under these settings, we find the three optimal access probabilities via maximizing the secondary throughput given a constraint on the primary throughput. We compare the performance of the explicit ACK and explicit NACK schemes and contrast them with schemes based on either sensing or primary ARQ feedback only.