Low Earth Orbit (LEO) nano-satellites can provide uplink connectivity for large numbers of distributed Internet of Things (IoT) sensing devices. To achieve a target Quality-of-Service (QoS), devices must send packets multiple times, due to collisions. This paper characterises the achievable set of terminal QoS targets, and determines the optimal uplink packet attempt rates. We show that QoS target feasibility is determined by the solution of a linear program (LP), and that the solution gives the optimal packet attempt rates. We show that the QoS targets can be modified using the shadow prices from the LP, to obtain feasibility. We show that our LP based approach can support greater than 30% more ground sensor terminals, compared to existing schemes.

This paper analyses the data message success rate for Internet of Things (IoT) sensing devices communicating over Low Earth Orbit (LEO) satellite links. We present an analytical framework for optimizing multi-objective multi-packet reception on the uplink. We present an analytical result for the probability of message success for a given ground terminal, and present an analytical result for the overall probability of message success, averaged across all terminals.

The achievable rate performance of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided massive multiple-input multiple-output (MIMO) systems is investigated. Specifically, the achievable user rates are derived for three operating protocols of the STAR-RIS, namely the energy-splitting (ES), mode-switching (MS), and time-switching (TS) with both unicast and multicast transmissions. This analysis is useful in evaluating the system performance under imperfectly estimated channel state information (CSI), spatially correlated fading, pilot contamination, and statistical CSI based phase-shift optimization, transmit power control, and user signal decoding. For the high signal-to-noise ratio regime, the asymptotic achievable rates are also derived, and they serve as benchmarks or upper bounds for the rate performance comparisons for systems operating under the above transmission impediments. The composite uplink channels are estimated through linear minimum mean square error estimation techniques, and the phase-shift matrices at the STAR-RIS are optimized to maximize the effective average channel gains to minimize the channel estimation overhead. The base-station optimizes the transmit power based on the max-min criterion to attain a system-wide common user rate by negating the near-far effects of the downlink composite channels. Our numerical and simulation results validate our theoretical analysis and convergence of phase-shift and transmit power optimization algorithms. Our analytical and simulation results are useful in investigating the performance gains/comparisons among the ES, MS, and TS protocols for unicast and multicast transmissions to enable 360° smart coverage extensions with passive STAR-RIS aided massive MIMO.

This paper designs energy-efficient trajectories for unmanned aerial vehicles (UAVs) harvesting data sequentially from distributed ground nodes. We propose a novel optimization framework for path planning, based on dynamic programming. We develop an optimum backward-forward algorithm that jointly optimizes the hovering locations for each ground node, and the visiting order to those locations. Our algorithm minimizes the total energy consumption of the UAV over its trajectory. Our framework is compatible with various probabilistic wireless communication channel models, and can also be applied to different cost functions, including minimising the total flying time, and allowing for bi-directional communications. We also develop a lower complexity algorithm that approximates the optimum UAV trajectory by decomposing the original problem into two sub-problems, and iterating back and forth between the two. This alternating algorithm has polynomial time complexity, and we show that it produces a near-optimum UAV trajectory, with as little deviation as 5% to 15% from the average energy consumption of the optimum algorithm.

We propose a graph-theoretic analytical framework to solve the sum rate maximization problem of a non-orthogonal-multiple-access (NOMA)-aided distributed millimeter wave massive multiple-input multiple-output (MIMO) system. The optimal solution for this system-wide sum rate maximization problem is neither mathematically tractable nor computationally-efficient when a traditional communication-theoretic analytical approach is solely invoked. Thus, the original problem is decoupled into two sub-problems, namely, a user access point (AP) association/clustering and a pilot resource allocation. In the first subproblem, APs optimally select a set of users having the highest average channel power gains, while the second sub-problem optimally assigns a set of limited orthogonal pilots among concurrently served users such that the pilot contamination is minimized. We propose a graph-theoretic analytical framework to find practically-viable and computationally-efficient solutions to both these sub-problems by virtue of modeling them via bipartite graph matching and vertex coloring problems. Thereby, we propose an algorithm to compute the minimum number of orthogonal pilots required for a given user-AP association/clustering. By exploiting the minimum pilot length and leveraging the benefits of our graph-theoretic approach, we propose a pragmatic solution of the coexistence of NOMA and orthogonal multiple-access schemes to further boost the achievable rate performance of our proposed system set-up.

The sum rate performance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR- RIS) assisted full-duplex (FD) communication systems is investigated. The reflection and transmission coefficients of STAR- RIS elements are optimized for the energy splitting and mode switching protocols to maximize the weighted sum rate of the system. The underlying optimization problems are non-convex, and hence, the successive convex approximation technique has been employed to develop efficient algorithms to obtain suboptimal solutions. Thereby, the maximum average weighted sum rate and corresponding coefficients at the STAR-RIS subject to predefined threshold rates and unit-modulus constraints are quantified. The performance of the proposed system design is compared with the conventional reflecting/transmitting-only RISs and half-duplex counterparts via simulations where it is observed that STAR-RIS can boost the performance of FD systems.

In this paper, the feasibility of adopting an intelligent reflective surface (IRS) in a cell-free wireless communication system is studied. The received signal-to-noise ratio (SNR) for this IRS-enabled cell-free set-up is optimized by adjusting phase-shifts of the passive reflective elements. Then, tight approximations for the probability density function and the cumulative distribution function for this optimal SNR are derived for Rayleigh fading. To investigate the performance of this system model, tight bounds/approximations for the achievable rate and outage probability are derived in closed form. The impact of discrete phase-shifts is modeled, and the corresponding detrimental effects are investigated by deriving an upper bound for the achievable rate in the presence of phase-shift quantization errors. Monte-Carlo simulations are used to validate our statistical characterization of the optimal SNR, and the corresponding analysis is used to investigate the performance gains of the proposed system model. We reveal that IRS-assisted communications can boost the performance of cell-free wireless architectures.

In this paper, the performance of a distributed intelligent reflective surface (IRS)-aided communication system is investigated. To this end, the optimal signal-to-noise ratio (SNR) achievable through the direct and reflected channels is quantified by controlling the phase-shifts of the distributed IRS. This optimal SNR is statistically characterized by deriving tight approximations to the exact probability density function and cumulative distribution function for Nakagami- m fading. Thereby, the outage probability and achievable rate bounds are derived in closed-form, and they are validated via Monte-Carlo simulations. Our numerical results reveal that the distributed IRS-aided communication set-ups can boost the outage and rate performance of wireless systems.

The performance of an intelligent reflecting surface (IRS)-assisted time-switching simultaneous wireless information and power transfer (SWIPT) system is investigated from a maxmin user-fairness perspective. A series of optimization problems is formulated to maximize the minimum harvested energy and the achievable user rates via jointly optimizing the transmit powers at the base-station (BS) and phase-shifts at the IRS. The underlying optimization problems are non-convex, and thus, the efficient alternating optimization algorithms are developed to obtain sub-optimal solutions. A combination of geometric programming and convex optimization techniques has been employed in an iterative manner to solve the transmit power allocation and IRS phase-shift optimization problems, respectively. Max-min based common/system-wide harvested energy and achievable user rate are characterized when the BS adopts linear/conjugate precoding. Thereby, a max-min fairness-based energy-rate trade-off is quantified. Our numerical results validate the proposed optimization solutions and reveal the underlying performance gains of the optimized system.

In this paper, an intelligent reflective surface (IRS)-aided multi-way communication system is investigated. The full-duplex reflective properties of the IRS are exploited to enable full-mutual data exchange among multiple users within a single channel-use. A max-min fairness-based achievable rate optimization problem is formulated to control the phase-shift matrix at the IRS. To this end, a minorization-maximization based iterative IRS phase-shift optimization algorithm is used to maximize the minimum achievable system-wide user rate. The simulation/numerical results are presented to validate the convergence of the proposed IRS phase-shift optimization algorithm and to investigate the system performance. Thereby, it is revealed that the proposed system mitigates the adverse near-far effects by ensuring a common system-wide achievable user rate while accomplishing full-mutual data exchange via intelligently controlling the phase-shifts at the IRS. Our numerical results reveal that the proposed system outperforms the current state-of-the-art multi-way communication system based on multiple-input multiple-output (MIMO) relays in terms of the achievable sum rate in the moderate-to-large IRS element regime.

A statistical characterization of the fundamental performance bounds of an intelligent reflective surface (IRS) intended for aiding wireless communications is presented. To this end, the outage probability, average symbol error probability and achievable rate bounds are derived in closed-form. By virtue of an asymptotic analysis in high signal-to-noise ratio (SNR) regime, the achievable diversity order is derived. Thereby, we show that a diversity gain in the order of the number of passive reflective elements embedded within the IRS can be achieved with only controllable phase adjustments. Thus, IRS has a great potential of boosting the wireless performance by intelligently controlling the propagation channels without employing additional active radio frequency chains.

The benefits of facilitating downlink (DL) channel estimation at users are investigated for multi-cell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. By leveraging channel reciprocity of time division duplexing mode and uplink channel estimates, the DL pilots are beamformed towards the clusters of NOMA users such that the DL pilot sequence length does not scale with the number of antennas at the base-station. This enables users to estimate their effective DL channels and adopt those to facilitate successive interference cancellation. Aiming at establishing the performance bounds, the achievable DL rates are derived based on the estimated DL channels, and thereby, the adverse joint impact of beamforming uncertainty, imperfect SIC, and residual intra/inter-cluster/cell interference is quantified. The residual inter-cell interference is mitigated by using pilot contamination precoding and the underlying asymptotically achievable user rates are derived. Based on our analytical results, we conclude that DL channel estimates at user nodes are essential in successfully reaping benefits of integrating NOMA with massive MIMO.

The performance of non-orthogonal multiple-access (NOMA)-aided massive multiple-input multiple-output (MIMO) uplink is investigated. Spatially-distributed user nodes are grouped into multiple clusters based on spatial-directional information, and a set of orthogonal pilots are assigned to these clusters. To strike a balance between the pilot training overhead and the number of users that can be served simultaneously in the same time-frequency resource block, the NOMA-enabled users within a given cluster share the same pilot sequence. The uplink channels are estimated at the massive MIMO base-station (BS) by using the pilots transmitted by the user nodes in all clusters. A computationally-efficient maximal ratio combiner is constructed at the BS via the estimated uplink channels. The achievable rates of this system set-up are derived for both finite and infinite BS antenna regimes. Thereby, the effects of imperfectly estimated channel state information, intra-cluster pilot contamination, and imperfect successive interference cancellation are analytically quantified. In order to guarantee user-fairness and thereby to mitigate near-far effects in uplink NOMA transmissions, a max-min transmit power control is invoked. Thereby, max-min fairness optimal transmit power control coefficients are derived. These power control coefficients depends only on statistical knowledge of downlink channels. Thus, the proposed transmit power control can readily be implemented at NOMA user nodes, which primarily rely on channel hardening with no downlink pilots are being transmitted by the BS in an attempt to minimize the training overhead. Our analysis and numerical results reveal that the proposed system can be exploited to enable massive access by optimizing the fundamental tradeoff among the number of simultaneously served NOMA users, uplink achievable rates and implementation complexity.

The impact of low-dimensional digital precoding on the achievable sum rate of a training-based massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system with a limited number of radio-frequency (RF) chains is investigated. A low-dimensional digital precoder is cascaded with a high-dimensional analog precoder and thus reducing the number of RF chains required at the NOMA-enabled massive MIMO base-station. Uplink channel cascaded with the analog precoder is estimated at the BS via pilots sent by the clustered users. Each cluster is allocated with an orthogonal pilot sequence, and it is shared among users within a cluster. The achievable downlink sum rate is derived by capturing the effects of practical impediment, including channel estimation errors, intra-cluster pilot contamination, imperfect successive interference cancellation, and statistical/partial channel knowledge at the users for signal decoding. Thereby, the sum rate degradation caused by these transmission impairments and the impact of reduced number of RF chains at the BS are quantified. Moreover, the achievable sum rate and the number of users that can be served simultaneously in the same time-frequency resource block by massive MIMO NOMA are compared with those of massive MIMO orthogonal multiple access (OMA). Our results are used to draw system-design insights on fundamental trade-offs between the number of simultaneously served users, achievable sum rates and computational complexity. We conclude that massive MIMO NOMA is practically-viable for supporting massive access with low rates requirements, whereas massive MIMO OMA is more desirable when the high rate requirement is more prevalence than the demand for massive access.

The coherence residual interference caused by intra-cell pilot contamination has been shown to be the most detrimental impediment to the underlay spectrum-sharing in massive multiple-input multiple-output (MIMO) systems. As a remedy, a reverse time-division-duplexing (R-TDD) transmission strategy is proposed for mitigating intra-cell pilot contamination. To this end, the performance of multi-cell multi-user underlay spectrum-sharing MIMO with R-TDD is investigated. The secondary transmit power constraints and achievable uplin/downlink sum rates are derived for both finite and infinite base-station (BS) antenna regimes. Thereby, the joint detrimental effects of spatial correlation, beamforming uncertainty caused by imperfectly estimated channels, inter-cell co-channel interference (CCI) and residual interference incurred by inter-cell pilot contamination are analytically quantified and compared against the conventional TDD (C-TDD) counterpart. Our analysis reveals that the secondary performance metrics with imperfectly estimated channels become independent of primary interference threshold when the number of BS antennas grows without bound. Thus, the secondary system can be operated in its maximum average transmit power independent of the primary system without hindering its asymptotic achievable rates. This is because the intra-cell coherence CCI of two systems can be asymptotically mitigated by exploiting R-TDD and asymptotic channel orthogonality in massive MIMO systems.

The feasibility of adopting distributed massive multiple-input multiple-output (D-MMIMO) for mitigating the achievable rate loss incurred by spatially-correlated fading at space-constrained user antenna arrays when served by co-located massive MIMO (C-MMIMO) counterpart is investigated. In order to explore a practically-viable design, the cumulative effects of spatial correlation at both multi-antenna access points (APs) and multi-antenna users, beamforming uncertainty caused by imperfectly estimated channel state information (CSI), and adverse effects of pilot contamination have been taken into account. The achievable user rates of the proposed D-MMIMO downlink operating in a multi-cell set-up are derived for finite/infinite antenna array regimes at the distributed APs. Thereby, the achievable user rates for C-MMIMO counterpart are deduced and compared with those of the proposed D-MMIMO downlink. Through a rigorous analysis and simulation results, we show that the distributed transmissions rendered by D-MMIMO can provide significant performance gains compared to the C-MMIMO counterpart (i) by minimizing adverse effects of spatial correlation via distinct spatial-directions/signatures rendered by distributed transmissions, (ii) by mitigating shadow fading via leveraging macro-diversity gains, and (iii) by reducing the average path-losses through effectively shortening transmission distances. Moreover, we reveal that the proposed D-MMIMO can be exploited to recover a significant portion of rate loss incurred by the lost of asymptotic channel orthogonality due to doubly-correlated channels when both APs and user nodes are equipped with space-constrained multiple-antenna arrays.

The detrimental effects of pilot contamination attacks by active eavesdroppers are investigated for multi-cell multi-user massive multiple-input multiple-output (MIMO) relay networks. To this end, secure transmission strategies are designed for both users-to-relay and relay-to-destination channels in the presence of active pilot attacks with imperfect legitimate user channel state information (CSI) and with no eavesdropper CSI knowledge at the relay. The excess degrees-of-freedom offered by the massive MIMO relay are exploited to mitigate detrimental effects of cooperative jamming (CJ) signals and to generate artificial noise (AN) during the first and second time-slots, respectively. Thereby, the achievable secrecy rate lower bounds for active attacks are derived in the finite/infinite relay antenna regimes. The secrecy rates for passive eavesdropping are deduced and compared to that of active attacks. A joint optimal power allocation scheme for the pilot, payload data, and AN and CJ signals is formulated and solved by using geometric programming techniques. Rigorous numerical and simulation results are provided to obtain valuable insights, which are useful in designing secure physical layer transmission strategies for multi-cell multi-user massive MIMO relay networks.

In this paper, the achievable secrecy rate of a relay-assisted massive multiple-input multiple-output (MIMO) downlink is investigated in the presence of a multi-antenna active/passive eavesdropper. The excess degrees-of-freedom offered by a massive MIMO base-station (BS) are exploited for sending artificial noise (AN) via random and null-space precoders. An active eavesdropper contaminates the uplink channel estimates by sending pilot sequences identical to those of the legitimate users/relay. This active pilot contamination makes the massive MIMO BS implicitly beamform the confidential signals toward the active eavesdropper during two-hop downlink transmissions. The achievable secrecy rates are derived by taking the detrimental effects of actively contaminated channel state information with estimation errors and spatially correlated fading at the multiple-antenna terminals into account. The secrecy rate loss incurred by active pilot attacks over passive eavesdropping is investigated, and the secrecy rate gap between random and null-space-based AN is compared. A novel transmit power control policy is designed to efficiently allocate transmit power at the BS/relay for payload data and AN sequences for maximizing the achievable secrecy rate. Our results reveal that active pilot contamination attacks significantly degrade the achievable secrecy rate in dual-hop transmissions, and the corresponding detrimental effects cannot be asymptotically mitigated in the infinite BS antenna regime.

Multi-cell multi-user underlay spectrum-sharing massive multiple-input multiple-output systems operating with reverse time division duplexing (R-TDD) are investigated. By primarily aiming at fully mitigating intra-cell pilot contamination and coherent interference, in the proposed R-TDD scheme, the primary/secondary systems are allowed to operate only in the opposite transmission directions. In order to establish fundamental performance limits, the secondary transmit power constraints and achievable rates are derived in the presence of training-based channel estimation. Thereby, the joint detrimental effects of spatial correlation, beamforming uncertainty, and inter-/intra-cell coherence interference due to pilot contamination are quantified and compared against the conventional TDD (C-TDD) counterpart. A max-min optimal power control policy is designed, and thereby, the common achievable rates and power control coefficients are derived. It is shown that by invoking R-TDD, the secondary power constraints and sum rates can be made to become asymptotically independent of primary interference threshold. Thus, the secondary system can be operated with its maximum average transmit power, independent of the primary system without hindering its asymptotically achievable rates by the virtue of the inherent intra-cell coherent interference mitigation benefit of the R-TDD. By exploiting the pilot decontamination feature of R-TDD, the achievable rates of primary/secondary systems can be significantly boosted, compared to the underlay spectrum sharing with C-TDD.

The performance of multi-cell massive multiple-input multiple-output (MIMO) downlink (DL) with nonorthogonal multiple access (NOMA) is investigated. A novel pilot assignment for NOMA users is proposed to mitigate intra-cluster pilot contamination and to alleviate the detrimental impact of inter-cluster in the useful operating regime. The user nodes grouped into the same cluster based on the spatial-direction information are assigned orthogonal pilots to mitigate intracluster pilot contamination. These pilots are shared among the clusters to reduce the training overhead. However, the intercluster pilot contamination is mitigated by exploiting the distinct spatial directions of clusters. This pilot assignment ensures that the number of NOMA users that can be served simultaneously in the same time-frequency-spatial resource block is not limited by the length of coherence interval. A max-min fairness optimal transmit power allocation algorithm is proposed to guarantee user-fairness. The achievable DL sum rates are derived, and thereby, the cumulative impact of channel estimation errors, imperfect successive interference cancellation, and statistical channel knowledge at users is quantified. Our analysis reveals that the proposed massive MIMO NOMA can support massive access without compromising the spectral efficiency gains.

The achievable secrecy rates for a cell-free massive multiple-input multiple-output (MIMO) in the presence of a single-antenna active eavesdropper are derived and compared with that of co-located massive MIMO. The active eavesdropper contaminates the uplink channel estimates at the access points (APs), and the downlink precoders at the APs are constructed based on this contaminated channel estimates. Hence, the APs inadvertently beamform confidential information towards the active eavesdropper during the downlink transmission. The achievable rates at the legitimate user nodes and the rates leaked into the active eavesdropper are derived for estimated/imperfect channel state information in the finite and infinite regimes of the number of APs. A transmit power allocation algorithm for maximizing the achievable secrecy rate is developed. Our analysis and numerical results reveal that the rate leaked into the eavesdropper can be of significance in the presence of active pilot attacks in cell-free massive MIMO.

The asymptotic performance of multi-user multi-hop massive multiple-input multiple-output (MIMO) relay networks, in which multiple spatially distributed user nodes communicate with a multiple-antenna destination via multi-antenna amplify-and-forward relays, is investigated. To this end, the signal-to-interference-plus-noise ratio and achievable sum rate expressions are derived for three specific antenna configurations at the relay and destination nodes, namely (i) infinitely many relay and destination antennas, (ii) finitely many relay antennas and infinitely many destination antennas, and (iii) finitely many relay and destination antennas. The asymptotic achievable sum rates are derived when the numbers of antennas at the relays and destination grow unbounded while keeping fixed ratios among them. The upper and lower bounds of the average sum rates are derived for the finite antenna regime by using bounds for harmonic mean and Jensen's inequality. The detrimental effects of channel estimation errors are investigated by deriving the achievable sum rate expressions. Our sum rate analysis reveals that the massive MIMO technology can be exploited to achieve significant spectral efficiency gains for multi-hop relay networks.

The performance of massive multiple-input multiple-output (MIMO) configurations for multi-cell multi-user amplify-and-forward relay networks is investigated. Two massive MIMO signal processing techniques (SPT-1 and SPT-2) are proposed, and their computational complexity and performance gains are compared with multi-pair massive MIMO relaying (SPT-3) by deriving the partial and full achievable asymptotic sum rates for perfect and imperfect channel state information. Thereby, the cumulative effects of channel estimation errors, co-channel interference (CCI), and pilot contamination are investigated. The best relay selection for the multi-branch relaying is investigated. An optimal user power control algorithm based on max-min fairness is formulated, and thereby, the power allocation co-efficients and optimal common achievable user rate are derived in closed-form. Our analysis reveals that the effects of pilot contamination for the multi-cell massive MIMO relaying become more severe than that for the single-hop counterpart due to the multiple sources of CCI and propagation of residual interference in dual-hop transmissions.

The achievable sum rate of multi-cell multi-user massive multiple-input multiple-output (MIMO) downlink is investigated. Hybrid beamformers, which are constructed by using estimated/imperfect channel state information, are employed at the base-station. Thus, the massive MIMO base-station in each of the L cells is enabled with a typical power-intensive digital precoder and a quantized analog beamformer. This setup can significantly reduce the number of radio frequency (RF) chains required for the digital precoder, and thereby, the power consumption in the digital signal processing and the complexity of circuitry at the massive MIMO base-station. The achievable asymptotic sum rate expressions are derived for linear digital precoders namely, (i) zero-forcing transmission and (ii) maximum ratio transmission cascaded with a phase-shifting analog beamformer. Thereby, the asymptotic sum rate degradation due to the hybrid beamforming is quantified and compared against the full-dimensional digital beamforming. This sum rate loss is a function of the number of phase quantization levels and cannot be canceled completely even in the asymptotic base-station antenna regime. Nevertheless, our analysis reveals that the detrimental effects of phase quantization and reduced number of RF chains of the hybrid beamforming can be mitigated in the limit of increasingly many base-station antennas when the receiver thermal noise power is negligibly smaller than the residual interference due to pilot contamination.

The sum rate performance of simultaneous relay and direct massive multiple-input multiple-output (MIMO) downlink transmissions is investigated for the imperfect channel state information (CSI) case. In this context, a massive MIMO base-station transmits simultaneously to spatially distributed relayed and direct user nodes by using two independent zero-forcing based precoders. The uplink CSI at the base-station is estimated by using the non-orthogonal pilots transmitted by relayed and direct user nodes, and hence, the channel estimates are affected by pilot contamination. To this end, the detrimental effects of intra-cell pilot contamination are quantified by deriving the achievable sum rates when the number of antennas at the base-station is allowed to grow without bound. Furthermore, the performance degradation due to concurrent relayed and direct transmissions is quantified by analyzing the intra-cell interference effects for imperfect CSI cases. Our analysis and numerical results reveal that massive MIMO can be exploited for spectral efficient aggressive spatial multiplexing for simultaneous relayed and direct downlink transmissions.