In this paper, we consider a two-way wiretap channel in the presence of Multi-Input Multi-Output Multi-antenna Eve (MIMOME), where both nodes (Alice and Bob) transmit and receive in an in-band full-duplex manner and Eve tries to decode the information exchanged between Alice and Bob. For this system with keyless security, we provide artificial noise (AN) based signal design while assuming AN to be known/unknown at the receivers (Alice and Bob). Power allocation problems are formulated with the objective of maximization of sum secrecy rates subject to the transmit power constraint. Accounting for non-ideal self-interference cancellation, a successive convex approximation-based algorithm for the solution for the cases of full CSI, and limited CSI knowledge, including partial information at Eve. For the limited CSI case, numerical ergodic rates are optimized. Simulation results show that with power constraints satisfied, secrecy rates are maximized, and with only partial information at Eve, secrecy rates are improved accordingly.

With the increasing number of applications of Internet of Things (IoT) devices, co-channel interference is unavoidable among coexisting wireless networks, where multiple IoT devices transmit their observations to their respective destinations [access points (APs)]. In this scenario, we present a joint precoding and power allocation solution to minimize the mean squared error (MSE), while satisfying power constraints at individual IoT devices. In this regard, first, the necessary feasibility condition for the joint convexity of the optimization problem is derived, ensuring the global optimum solution. Subsequently, based on the solution, an iterative MSE algorithm is formulated and analyzed for convergence. The expressions for the MSE-based precoder is obtained via solving Karush–Kuhn–Tucker (KKT) conditions. Further analysis shows that the total resulting MSE at APs is limited by the observation signal-to-noise-ratio (SNR). It leads to the inference that in order to avoid the MSE saturation at APs at higher SNRs, the transmit power at IoT devices should be scaled proportional to and less than the observation SNR. Next, we compare the performance of our solution with two classical methods, namely, the minimum variance distortionless precoding (MVDP) and interference alignment (IA) methods, which are modified and enhanced for the given system. Simulations verify the above inference, and the global convergence of the MSE algorithm, with robustness to initializations yielding the better precoders and power allocation as compared to MVDP’s and IA’s in terms of the averaged total MSE performance.

In this paper, we study the benefits of in-band full-duplex (IBFD) and multi-input multi-output (MIMO) systems on the physical layer-based secret key generation (PHY-SKG) scheme. The secret key capacity (SKC) of a channel frequency response (CFR)-based PHY-SKG scheme in IBFD-MIMO systems is derived with practical imperfections and compared to its half-duplex (HD) counterpart. The results demonstrate that IBFD benefits the SKC over HD in two aspects: 1) simultaneous measurements; 2) more resources for probing to reduce errors. However, the benefits are compromised by the overheads and imperfections of self-interference cancellation (SIC). MIMO systems can significantly improve the SKC, while enlarged antenna arrays result in increased probing errors. Besides, longer overheads will be required to implement effective SIC, decreasing the IBFD gain over HD. 3GPP specification-based simulations verify the theoretical analysis.

Physical layer-based secret key generation (PHY-SKG) schemes have attracted significant attention in recent years due to their lightweight implementation and ability to achieve information-theoretical security. In this paper, we study a channel frequency response (CFR)-based SKG scheme for in-band full-duplex (IBFD)-multi-input and multi-output (MIMO) systems. We formulate the intrinsic practical imperfections and derive their effects on the probing errors. Then we derive closed-form expressions for the secret key capacity (SKC) in the presence of a passive eavesdropper accordingly. We analyze the asymptotic behavior of the SKC in the high-SNR regime and reveal the fundamental limits for IBFD and HD probing. Based on the asymptotic SKC, we investigate the conditions under which IBFD can outperform HD. Numerical results illustrate that effective analog self-interference cancellation (ASIC) depth is the basis for IBFD probing to gain benefits over HD. Finally, we analyze the properties of the collected samples of the CFR-based SKG scheme and propose an averaging pre-processing and a segmental quantization, which reduce the key disagreement rate and remove the effects of large-scale fading to guarantee randomness. 3GPP specification-based simulations and the National Institute of Standards and Technology (NIST) test suite verify the theoretical analysis and the effectiveness of the proposed SKG scheme.

This paper proposes a 3GPP-inspired design of the in-band-full-duplex (IBFD) integrated access and backhaul (IAB) networks for integrated sensing and communications (ISAC) in the frequency range 2 (FR2) band for vehicle-to-everything (V2X) communications. Unlike half-duplex (HD), IBFD-IAB-nodes (i.e., roadside units) in this work are doing sensing and communications at the same time and frequency, which enhance the spectral efficiency (SE) and sensing accuracy, as well as reduce the latency. With the help of the extended Kalman filter (EKF), radar sensing is performed at the IBFD-IAB-nodes for the multi-vehicle scenario. Assuming the high power self-interference (SI) at the IBFD-IAB-node has been successfully canceled in the propagation, analog, and digital domains, only the residual SI is reserved in this work. Numerical results show that the closer the vehicle to the IAB-node, the higher the SE it can get. Compared with HD ISAC-IAB systems, our proposed IBFD ISAC-IAB systems can double the SE at the vehicle.

In this paper, a generalized superimposed training (GST) scheme is used for uplink cell-free massive multiple-input multiple-output (mMIMO) systems to mitigate the pilot contamination effect, where multiple users are served by various access points (APs). In the proposed scheme, the pilots and data sequences are superimposed entire coherence time using a novel precoding technique. We consider variable data length in the Rician fading environment and estimate the channel using the least-squares (LS) estimator, and subsequently, the data are estimated. Moreover, we derive an expression for signal-to-interference-plus-noise ratio (SINR) for the proposed GST scheme. The results are given in terms of bit error rate (BER), sum-rate, and normalized mean-squared error (NMSE) of the channel estimation. We compare our proposed GST scheme, the conventional superimposed training (ST), and the regular pilots (RP) scheme, and showed the benefit of the proposed approach. Finally, two receiver cooperation levels are considered, including fully centralized processing and localized processing. We realize lower NMSE and BER but the higher value of sum-rate in fully centralized processing with the GST scheme.

This paper proposes a generalized superimposed channel estimation scheme for an uplink cell-free massive multiple-input multiple-output (mMIMO) system, which is aided by several reconfigurable intelligent surfaces (RIS) to enhanced performance in terms of coverage and spectral efficiency. We consider that the system has both direct links (between access points (APs) and users) and indirect links through each RIS. The estimated channels are used to detect the data streams, and consequently, bit error rate (BER) and sum-rate performances are evaluated. Moreover, we optimize phase shift coefficients to minimize the channel estimation error statistics. Two levels of receiver cooperations (fully centralized processing and local processing) are considered in this work. Simulation results show that the RIS-aided cell-free mMIMO system with the optimal RIS phase coefficients can effectively improve the channel estimates as compared to those without RIS or with randomly phased RISs. It is also verified that fully centralized processing provides much lower channel estimation normalized mean-square error (NMSE) and BER than that for local processing, and confirmed that generalized superimposed training (GST) scheme shows the better performance in channel estimates compared with the standard superimposed training (ST) and the regular pilots (RP) scheme.

Federated learning (FL) is an emerging distributed learning algorithm where the process of data acquisition and computation are decoupled to preserve users’ data privacy. In the training process, model weights have to be updated at both base station (BS) and local users sides. These weights, when exchanged between users and BS, are subjected to imperfections in uplink (UL) and downlink (DL) transmissions due to limited reliability of wireless channels. In this paper, for a FL algorithm in a single-cell massive MIMO cellular communication system, we investigate the impacts of both DL and UL transmissions and improve the communication-efficiency by adjusting global communication rounds, transmit power and average codeword length after quantization. Simulation results on standard MNIST dataset with both i.i.d and non-i.i.d training data distributions are also presented. Our simulation results have shown accelerated learning for various local steps and transmit power. The network energy consumption has been reduced while achieving similar testing accuracy at higher iterations.

In this paper, the problem of distributed content caching in a small-cell Base Stations (sBSs) wireless network that maximizes the cache hit performance is considered. Most of the existing works consider static demands, however, here, data at each sBS is considered to be correlated across time and sBSs. Federated learning (FL) based caching strategy is proposed which is assumed to be a weighted combination of past caching strategies of the sBS as well as the neighbouring sBSs. A high probability generalization guarantees on the performance of the proposed federated caching strategy is derived. The theoretical guarantee provides following insights on obtaining the caching strategy: (i) run regret minimization at each sBS to obtain a sequence of caching strategies across time, and (ii) maximize an estimate of the bound to obtain a set of weights for the caching strategy which depends on the discrepancy. Theoretical guarantee on the performance of the least recently frequently used (LRFU) caching strategy is derived. Further, FL based heuristic caching algorithm is also proposed. Finally, it is shown through simulations using Movie Lens dataset that the proposed algorithm significantly outperforms the recent online learning algorithms.

Regular pilots in a massive multi-input multi-output (MIMO) system with large number of users suffer from the pilot contamination effect due to limited training time. In this paper, for a cell-free massive MIMO system, we have proposed a generalized superimposed pilot (GSP) scheme, where the available number of pilots are equal to the coherence time slots, and the transmitting data symbols are spread over the coherence time with the help of simple precoding. Further, in order to keep the system scalable, a low complexity and distributed time processing approach is employed, and the corresponding rate components are analyzed. It is shown that with careful design of precoding matrix and number of data symbols, the GSP symbols can provide much better channel estimation and data detection performance, as compared to the regular pilot scheme and the conventional superimposed scheme. These results have been verified via simulations. It is also inferred that centralized processing in cell free system improves the data detection performance than localized processing. Iterative data detection at the central node also improves the MSE of data estimates. The pilot contamination effect, is significantly reduced due to availability of larger number of pilots, as compared to regular pilots transmission.

This paper develops a 3GPP-inspired design for the in- band-full-duplex (IBFD) integrated access and backhaul (IAB) networks in the frequency range 2 (FR2) band, which can enhance the spectral efficiency (SE) and coverage while reducing the latency. However, the self-interference (SI), which is usually more than 100 dB higher than the signal-of-interest, becomes the major bottleneck in developing these IBFD networks. We design and analyze a subarray-based hybrid beamforming IBFD-IAB system with the RF beamformers obtained via RF codebooks given by a modified Linde-Buzo-Gray (LBG) algorithm. The SI is canceled in three stages, where the first stage of antenna isolation is assumed to be successfully deployed. The second stage consists of the optical domain (OD)-based RF cancellation, where cancelers are connected with the RF chain pairs. The third stage is comprised of the digital cancellation via successive interference cancellation followed by minimum mean-squared error baseband receiver. Multiuser interference in the access link is canceled by zero-forcing at the IAB-node transmitter. Simulations show that under 400 MHz bandwidth, our proposed OD-based RF cancellation can achieve around 25 dB of cancellation with 100 taps. Moreover, the higher the hardware impairment and channel estimation error, the worse digital cancellation ability we can obtain.

Wireless edge caching is an important strategy to fulfill the demands in the next generation wireless systems. Recent studies have indicated that among a network of small base stations (SBSs), joint content placement improves the cache hit performance via reinforcement learning, since content requests are correlated across SBSs and files. In this paper, we investigate multi-agent reinforcement learning (MARL), and identify four scenarios for cooperation. These scenarios include full cooperation (S1), episodic cooperation (S2), distributed cooperation (S3), and independent operation (no-cooperation). MARL algorithms have been presented for each scenario. Simulations results for averaged normalized cache hits show that cooperation with one neighbor (S3) can improve the performance significantly closer to full-cooperation (S1). Scenario 2 shows the importance of frequent cooperation, when the level of cooperation is high, which depends on the number of SBSs.

The full-duplex (FD) integrated access and backhaul (IAB) technique is considered in the 3rd Generation Partnership Project (3GPP) release 16 to address the demand for high capacity, large coverage and low latency millimeter wave (mmWave) beyond 5G networks. However, the self-interference (SI) appears on the FD-IAB-node, becoming the major bottleneck in the development of these novel networks. With the assumption of successful antenna isolation, this paper proposes an SI cancellation (SIC) technique for the wideband mmWave-FD-IAB multiuser networks with the cost-efficient subarray hybrid precoding structure. The fiber Bragg grating-based analog canceler is established between the RF chain pairs of the FD-IAB-node, and the minimum mean square error baseband combiner is used at the FD-IAB-node receiver to cancel the residual SI digitally. Further, hardware impairment (HWI) is considered to capture the transceiver distortion. Simulation results show that the proposed FD-IAB network capacity is almost doubled compared to the half-duplex-IAB system when the effect of HWI is low.

The caching of popular contents in a cellular network is known to reduce the data load in the backhaul link, and have been an active area of research. This paper considers the problem of efficient distributed content coded caching in a small-cell Base Station (sBS) wireless network to improve the cache hit performance. The demands at each sBS across time and sBSs is assumed to be correlated, and is unknown. A new weighted (across time and sBS) caching strategy is proposed. A high probability lower bound on the cache hit is derived, which is obtained using the proposed strategy as a function of the cache hit of the optimal caching strategy. The bound is shown to depend on (i) the weighted average of cache hits, (ii) regret, and (iii) the discrepancy across time and sBSs (a measure of correlation of demands across time and sBSs). This provides the following insight on obtaining the caching strategy: (i) find a sequence of caching strategies by running regret minimization across time at each sBS, and (ii) maximize an estimate of the bound to obtain a set of weights. The insight is shown to result in an iterative distributed algorithm to obtain caching strategies at each sBS. The performance of the proposed caching strategy is shown to outperform Least Recently Frequently Used (LRFU) algorithm by a large margin.

Integrated Access and Backhaul (IAB) systems, millimeter-wave (mmWave), and full-duplex (FD) are the critical technologies for the 5G and beyond cellular networks. The capacity and efficiency are significantly enhanced, thanks to the large available bandwidth and simultaneous transmission. However, self-interference (SI) becomes significant, which needs to be canceled in different stages. In this paper, under the mmWave-FD-IAB systems, we propose a fast hybrid precoding algorithm using the Nyström method for the wideband single-user scenario. SI and residual SI (RSI) is canceled by RF precoder and minimum mean squared error combiner at the IAB-node transmitter and receiver, respectively. RF insertion loss with different kinds of phase shifters (PSs) and channel estimation error are considered for accounting the impact of the RF components impairment and channel uncertainty. Simulations show that the proposed design can enhance the backhaul link spectral efficiency and cancel the SI efficiently at the IAB-node. Moreover, passive PSs can tolerate more RSI than active PSs.

Caching popular contents in advance is an important technique to achieve low latency and reduced backhaul congestion in future wireless communication systems. In this article, a multi-cell massive multi-input-multi-output system is considered, where locations of base stations are distributed as a Poisson point process. Assuming probabilistic caching, average success probability (ASP) of the system is derived for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. Further, modeling CP variations across time as a Markov process, reinforcement Q-learning is employed to learn the optimal content placement strategy to optimize the long-term-discounted ASP and average cache refresh rate. In the Q-learning, the number of Q-updates are large and proportional to the number of states and actions. To reduce the space complexity and update requirements towards scalable Q-learning, two novel (linear and non-linear) function approximations-based Q-learning approaches are proposed, where only a constant (4 and 3 respectively) number of variables need updation, irrespective of the number of states and actions. Convergence of these approximation-based approaches are analyzed. Simulations verify that these approaches converge and successfully learn the similar best content placement, which shows the successful applicability and scalability of the proposed approximated Q-learning schemes.

Large number of redundant requests in wireless networks have led to the hybrid satellite-terrestrial networks, where a satellite is used for content placement at edge caches at the base stations (BSs), thereby reducing backhaul link usage. In this paper, we consider in-network caching where an unavailable content at one BS can be fetched from the nearest BS in the network, before requesting from the content server. Obtaining optimal placement incurs exponentially huge computational overhead. Recent caching solutions are not scalable for large size of content library. Therefore, we propose a low-complexity approach using an action-coded deep deterministic policy gradient (AC-DDPG) algorithm towards optimizing the long-term average network delay. The proposed approach employs continuous valued popularity profiles rather than a fixed finite set in the literature. Simulation results demonstrate the successful application of proposed approach and the improvement over the most-popular content caching method.

Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-the-leader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.

In this paper, we consider interference channel model in which transmissions from multiple users are partially correlated. This correlation arises in wireless sensor network (WSN) scenarios and temporally correlated models. Considering this model, two minimum mean squared error (MSE) based precoding methods are derived. With these formulations, an iterative convergent procedure is formulated similar to a typical interference alignment (IA) algorithm. Simulations show that the second method provides the best sum rates for different correlation values.

To meet increasing demands of wireless multimedia communications, caching of important contents in advance is one of the key solutions. Optimal caching depends on content popularity in future which is unknown in advance. In this paper, modeling content popularity as a finite state Markov chain, reinforcement Q-learning is employed to learn optimal content placement strategy in homogeneous Poisson point process (PPP) distributed caching network. Given a set of available placement strategies, simulations show that the presented framework successfully learns and provides the best content placement to maximize the average success probability.

This paper presents two analog precoder feedback (PFB) schemes namely the simple PFB (SPFB) scheme and the reduced PFB (RPFB) scheme for interference channels. In the first scheme, each destination transmits full precoder, while in the second one, the size of the precoder to be fed back is reduced by removing the redundant rows of the precoder matrix. This reduced feedback is particularly useful in the systems, where the number of transmit antennas are more than the number of receive antennas. These schemes have been analyzed for the cases where time slots in the feedback duration are independent of the number of users and where the time slots scale linearly with the number of users. For both these schemes, the precoder reconstruction methods are investigated using the chordal distance. For interference alignment, the rate loss upper bound analysis shows that the rate loss increases with signal-to-noise-ratio (SNR) at low SNR, while it remains constant in high SNR range. Simulation results verify that the linear sum rate scaling is preserved at high SNRs. The full precoder feedback dominates, when the source cooperation is assumed, while RPFB yields improvements over SPFB scheme, when no source cooperation is assumed and$Kd$time slots are utilized in either orthogonal or independent transmission case.

Interference Alignment is a promising technique for achieving higher rates by aligning interference at the receiver. To design such a system, the global channel state information at the transmitter (CSIT) as well as at the receiver is necessary. But in practice, it is hard to obtain this information, therefore, limited feedback is used to provide CSIT or the precoder design information to the transmitter. Conventionally, precoders are quantized at receiver by finding its best match in the codebook using chordal distance and its index is fedback to the transmitter. In this paper, instead of minimizing chordal distance, we propose algorithms with objectives that are derived from subspace alignment method, SINR maximization, or minimization of leakage interference power to measure the “goodness” of quantized vector. These algorithms achieve higher rates for small size codebooks. The rate loss has been analyzed for precoder quantization. We also find less computational intensive solution to find the desired vectors in the codebook. The simulation results show that for small codebooks, significant sumrate gains can be achieved for (2 × 2,1)3for 2–6 bits of feedback per user, compared to quantization based on chordal distance, while for large codebooks, the chordal distance based quantization performs better.