ABSTRACT Battery‐limited devices and time‐sensitive applications are considered as key players in forthcoming wireless sensor network. So, the main goal of the network is two‐fold; charge battery‐limited devices, and provide status updates to users where information‐freshness matters. In this paper, a multi‐antenna base station (BS) in assistance of simultaneously‐transmitting‐and‐reflecting reconfigurable intelligent surface (STAR‐RIS) transmits power to energy harvesting (EH) devices while controlling status update performance at information users by analysing age of information (AoI) metric. Therefore, we derive a scheduling policy at BS, and analyse joint transmit beamforming and amplitude‐phase optimization at BS and STAR‐RIS, respectively, to reduce average sum‐AoI for the time‐sensitive information users while satisfying minimum required energy at EH users. Moreover, two different energy‐splitting and mode‐switching policies at STAR‐RIS are studied. Then, by use of an alternating optimization algorithm, the optimization problem is studied and non‐convexity of the problem is tackled by using the successive convex approximation technique. Through numerical results, AoI‐metric and EH requirements of the network are analysed versus different parameters such as number of antennas at BS, size of STAR‐RIS, and transmitted power to highlight how we can improve two‐fold performance of the system by utilizing STAR‐RIS compared to the conventional RIS structure.

To support object detection and tracking as one of the main services of a mono-static integrated sensing and communications (ISAC), an ISAC node needs to operate in full-duplex mode, and therefore suffers from strong self-interference (SI) mainly due to mutual coupling (MC) between its antenna arrays. This paper studies beamforming (BF) designs at transmitter (Tx) and/or receiver (Rx) sides of ISAC node to support both multibeam transmissions for simultaneous communications and sensing, and also manage SI cancellation (SIC) over signaling bandwidth and suppress specific spatial reception angles. Provided that ISAC node has estimated SI channel only over a few frequency indices, a null-space projection (NSP) based BF designs at Tx-only, Rx-only, and joint Tx-Rx are studied to see which one is preferred from hardware-efficiency perspective. Moreover, a system level analysis with a proposed sensing frame structure is discussed to support reliable and periodic sensing of a region by choosing proper parameters while minimizing communications performance loss. Performance of SIC and effect of number of its constraints are discussed through numerical results. It is shown than NSP-based Tx-BF is sufficient for SIC and one can relax RxBF by only utilizing simple phase-shifters. Moreover, importance of having precise estimation of MC, and performance of object detection with and without SIC are discussed.

Precise positioning plays a critical role in beyond-fifth-generation ($\mathbf{B 5 G}$) wireless networks for a wide range of indoor and outdoor applications. Conventionally, positioning accuracy can be dramatically degraded in a high Doppler environment and it may also require complex signal processing technique to accurately estimate the positioning in harsh multipath channels. Thus, this paper studies a positioning reference signal with orthogonal time-frequency space (PRS-OTFS) to enhance multi-user positioning accuracy. Firstly, multi-user PRS are designed in delay-Doppler (DD) domain and then a simple DD-domain based algorithm is proposed to estimate time-of-arrivals (ToAs) which is mainly based on an efficient energy detection in DD domain. Performance superiority of the proposed DD-domain scheme compared to conventional time-frequency based estimators is also discussed. Finally, robustness of the ToA estimation algorithm is discussed through numerical results for various channel conditions and arbitrary non-integer normalized delay-and Doppler-spread.

Abstract Reflecting intelligent surface (RIS) is one of the key enabling technologies for beyond fifth generation wireless networks to further improve coverage, spectral‐ and energy‐efficiency of wireless networks. Recently, a novel simultaneous transmission and reflection RIS (STAR‐RIS) technology is introduced which enables more degrees‐of‐freedom in simultaneously serving users in reflection and transmission regions of RIS. This paper investigates a STAR‐RIS assisted two‐user full‐duplex communication system, wherein multi‐antenna base station (BS) and single‐antenna users are subject to maximum power constraints. Firstly, weighted sum rates are computed for three well‐known STAR‐RIS protocols, e.g. energy splitting, mode selection, and time splitting. Then, for each of the cases, weighted sum rate optimization problem is investigated to find optimal resource allocations, i.e. base station's precoding and combining matrices, coefficients of STAR‐RIS, and power allocations. The optimization problem is transferred into multiple convex sub‐problems using equivalent weighted minimum mean‐square‐error forms. Also, by use of the successive convex approximation method, tunable parameters of the STAR‐RIS are optimized. Although the original problem is a non‐convex one, proposed iterative alternating technique achieves an acceptable sub‐optimal performance. Finally, performance of the system is analysed and compared to some baselines to highlight superiority of the STAR‐RIS compared to the conventional RIS schemes.

Cell-free massive MIMO (CF-mMIMO) network and its user-centric (UC) version are considered as promising techniques for the next generations of wireless networks. However, fronthaul and backhaul assignments are challenging issues in these networks. In this paper, energy efficiencies of uplink transmission for the CF- and UC-mMIMO networks are studied, wherein access points (APs) are connected to aggregation nodes (ANs) through radio frequency (RF) and/or free-space optic (FSO) fronthauls, and the ANs are connected to a central processing unit via fiber backhauls. The achievable data rates are derived by taking into account the effects of hardware non-ideality at the APs and ANs, FSO alignment and weather conditions. To have a robust and energy-efficient network, especially in the presence of FSO misalignment and adverse weather conditions, first, a cognitive RF–FSO fronthaul assignment algorithm is proposed at the cost of sharing the available RF bandwidth between the access and fronthaul links. Then, optimal power allocations at the users and APs are investigated, and two analytical approaches are proposed to solve the non-convex optimization problem. Through numerical results, we have discussed how utilizing the cognitive RF–FSO fronthaul assignment achieves higher energy efficiency compared to that of FSO-only, RF-only, or simultaneously using RF and FSO fronthaul links, e.g., achieving up to$198\%$higher energy efficiency under unfavorable conditions. Moreover, the effects of FSO misalignment, weather conditions, and power allocations on the networks’ performances are discussed.

In this paper, uplink transmission of a cell-free massive multiple-input multiple-output (CF-mMIMO) system coexisting with device-to-device (D2D) communication links is investigated, under the assumption that access points (APs) are equipped with low resolution analog-to-digital converters (ADCs). Lower bounds of achievable rates for both D2D users (DUEs) and CF-mMIMO users (CFUEs) are derived in closed-form, with perfect and imperfect channel state information. Next, in order to reduce pilot contamination, greedy and graph coloring-based pilot allocation algorithms are proposed and analyzed for the considered scenario. Furthermore, to control interference and improve the performance, two power control strategies are designed and their complexity and convergence are also discussed. The first power control strategy aims at maximizing CFUEs’ sum spectral efficiency (SE) subject to quality of service constraints on DUEs, while the second one maximizes the weighted product of CFUEs’ and DUEs’ signal-to-interference-plus-noise-ratios (SINRs). Numerical results show that the proposed pilot and power allocations bring a considerable improvement to the network SE. Also, it is revealed that the activation of D2D links has a positive effect on the system throughput, i.e. the network offloading ensured by the D2D links overcomes the increased interference brought by D2D communications.

We studied power splitting-based simultaneous wireless information and power transfer (PS-SWIPT) in multiple access channels (MACs), considering the decoding cost and non-linear energy harvesting (EH) constraints at the receiving nodes to study practical limitations of an EH communication system. Under these restrictions, we formulated and analyzed the achievable rate and maximum departure regions in two well-studied scenarios, i.e., a classical PS-SWIPT MAC and a PS-SWIPT MAC with user cooperation. In the classical PS-SWIPT MAC setting, closed-form expressions for the optimal values of the PS factors are derived for two fundamental decoding schemes: simultaneous decoding and successive interference cancellation. In the PS-SWIPT MAC with user cooperation, the joint optimal power allocation for users as well as the optimal PS factor are derived. This reveals that one decoding scheme outperforms the other in the classical PS-SWIPT MAC, depending on the function type of the decoding cost. Finally, it is shown that the cooperation between users can potentially boost the performance of a PS-SWIPT MAC under decoding cost and non-linear EH constraints. Moreover, effects of the decoding cost functions, non-linear EH model and channel quality between the users are studied, and performance characteristics of the system are discussed.

Abstract Unmanned aerial vehicles have been widely used to assist wireless sensor networks due to ever‐increasing demands for Internet‐of‐things applications. To support timely delivery of information characterised by a recently introduced metric, termed as the age of information, this paper explores freshness of data in an unmanned aerial vehicle assisted wireless sensor network. Specifically, the authors consider a limited‐energy unmanned aerial vehicle moving towards the Internet‐of‐things devices to disseminate data packets provided by a data centre. Since the unmanned aerial vehicle cannot visit all the nodes in each flight turn due to its finite‐sized battery, the best sequence of nodes, from an age of information perspective, should be selected at the beginning of each flight turn. Thus, an unmanned aerial vehicle trajectory planning for data dissemination is proposed taking into account both maximal use of energy and freshness of data. To minimise the weighted sum age of information metric, by utilising the well‐known knapsack and travelling salesman problems, the authors propose an algorithm to efficiently select devices and the corresponding visiting order in each flight turn. Finally, to highlight performance of the proposed algorithm, and to investigate the effect of limited‐energy unmanned aerial vehicles, the number of nodes and flight turns, and simulation results are also provided and compared with other benchmark algorithms.

We study uplink of cell-free massive multiple-input multiple-output system with limited capacity fronthaul link connecting each access point (AP) to a central processing unit (CPU), under hardware impairments at user equipments and APs. Achievable rates are derived for three transmission strategies at APs, named as compress-forward-estimate (CFE), estimate-compress-forward (ECF), and estimate-multiply-compress-forward (EMCF), to efficiently share the fronthaul capacity for sending channel state information (CSI) and/or data signals of the users to the CPU. For forwarding the compressed version of the signals, low-complexity fronthaul rate allocations are proposed for ECF and EMCF strategies, which considerably improve the performance especially for limited capacity fronthauls. Besides, for CFE and ECF strategies, two power allocations are presented to maximize the sum rates; One approximate solution based on geometric programming and an alternative general iterative algorithm. It is shown that performances of the strategies are not the same, because they require different fronthaul rate allocations for CSI and/or data signals transmission to CPU, and their AP signal processing capabilities are different. Finally, to highlight performance characteristics of the proposed system under different transmission strategies and circumstances such as pilot reuse and multiple-antenna APs scenarios, numerical results are presented and the three strategies are compared.

The authors study a two‐user state‐dependent generalised multiple‐access channel (GMAC) with correlated states. It is assumed that each encoder has ‘non‐causal’ access to channel state information (CSI). They develop an achievable rate region by employing rate‐splitting, block Markov encoding, Gelfand–Pinsker multicoding, superposition coding and joint typicality decoding. In the proposed scheme, the encoders use a partial decoding strategy to collaborate in the next block, and the receiver uses a backward decoding strategy with joint unique decoding at each stage. The author's achievable rate region includes several previously known regions proposed in the literature for different scenarios of multiple‐access and relay channels. Then, they consider two Gaussian GMACs with additive interference. In the first model, they assume that the interference is known non‐causally at both of the encoders and construct a multi‐layer Costa precoding scheme that removes ‘completely’ the effect of the interference. In the second model, they consider a doubly dirty Gaussian GMAC in which each of interferences is known non‐causally only at one encoder. They derive an inner bound and analyse the achievable rate region for the latter model and interestingly prove that if one of the encoders knows the full CSI, there exists an achievable rate region which is ‘independent’ of the power of interference.

For a fading full‐duplex decode‐and‐forward relay channel, the authors analytically derive optimum power allocation schemes subject to individual power constraints at the source and the relay. The authors prove that the problem is a convex optimisation problem over the feasible power set, and by proposing a systematic technique to solve min–max problems, optimum power allocations are derived in closed‐forms. Finally, for a Rayleigh‐fading channel model, optimum power allocations are derived and the respective achievable rate and an upper bound are evaluated. The implications of the derived theoretical results are discussed through some graphs.

In this paper, lattice based coding is used over two-user Gaussian state-dependent multiple access channel with common message for two channel models. First, it is assumed that the additive channel interference is noncausally known at both encoders. Exploiting a lattice encoding and decoding scheme, the effect of the interference is completely removed and the capacity region is achieved. Then, the multiple access channel with two additive interferences is considered in the case, where each interference is known noncausally to the associated transmitter. A pure lattice based encoding and decoding structure is proposed and an inner bound on the capacity region is derived which is valid for the entire range of channel signal to noise ratio and powers of interferences.

In this study, a two-user discrete memoryless multiple-access channel with two correlated states known non-causally at the corresponding encoder is considered. Each encoder transmits a message independent of the other encoder's and tries to cooperate with it. To consider cooperative encoders, it is assumed that each encoder strictly-causally receives and finds out the other encoder's transmitted signals and tries to cooperate with the other encoder by transmitting its message. A special case is also studied in which the common part of the states is known at the both encoders, resulting in a larger rate region. For these two scenarios, achievable rate regions are derived using a combination of block-Markov and Gel'fand-Pinsker coding techniques, and outer bounds are also established. For the second scenario, the achievable rate region of the Gaussian model is derived, and some implications of the results are discussed.

In this paper, we present a new method for variable elimination in systems of inequalities which is much faster than the Fourier-Motzkin Elimination (FME) method. In our method, a linear Diophantine problem is introduced which is dual to the original problem. The new Diophantine system is then solved, and the final result is calculated by finding the dual system of inequalities. This new method uses the algorithm Normaliz to find the Hilbert basis of the solution space of the given Diophantine problem. We introduce a problem in the interference channel with multiple nodes and solve it with this new method. Next, we generalize our method to all problems involving FME and compare the method with the previous method. Our method has many advantages in comparison to the previous method. It does not produce many of the redundant answers of the FME method. It also solves the whole problem in one step whereas the previous method uses a step by step approach in eliminating each auxiliary variable.

In this paper, a two-user discrete memoryless multiple-access channel (DM-MAC) with correlated channel states, each known at one of the encoders is considered, in which each encoder transmits independent messages and tries to cooperate with the other one. To consider cooperating encoders, it is assumed that each encoder strictly-causally receives and learns the other encoder's transmitted symbols and tries to cooperate with the other encoder by transmitting its message. Next, we study this channel in a special case; we assume that the common part of both states is known at both, hence encoders use this opportunity to get better rate region. For these scenarios, an achievable rate region is derived based on a combination of block-Markov encoding and Gel'fand-Pinsker coding techniques. Furthermore, the achievable rate region is established for the Gaussian channel, and it is shown that the capacity region is achieved in certain circumstances.