In frequency division duplexing systems, downlink massive multiple-input multiple-output (MIMO) precoding algorithms rely on accurate channel state information (CSI) feedback from users. This paper investigates the tradeoff between the CSI feedback overhead and the resulting user performance in terms of achievable sum rate. Our approach consists of determining the precoding directly from the user feedback. We employ a deep learning-based design for an end-to-end precoding-oriented feedback architecture, including learned pilots, user compressors for finite-rate feedback, and base station processing to determine precoding vectors. We propose a novel loss function that maximizes the sum of achievable rates while minimizing the CSI feedback overhead. We consider both single- and multi-cell multi-user MIMO systems, analyzing the impact of intra- and inter-cell interference on the CSI feedback strategy design, as well as robustness. Simulation results demonstrate that our approach outperforms previous precoding-oriented methods and offers greater efficiency than conventional methods that separate CSI compression and precoding.

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. Based on this formulation, we derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. The analysis shows that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. To overcome this, a shift-of-origin transformation simplifies the optimization, leading to a closed-form expression for the parasitic reactance. The solution generalizes to arrays with multiple active and parasitic elements operating in multipath channels. The proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency.

This paper introduces a novel deep learning-based user-side feedback reduction framework, termedself-nomination. The goal of self-nomination is to reduce the number of users (UEs) feeding back channel state information (CSI) to the base station (BS), by letting each UE decide whether to feed back based on its estimated likelihood of being scheduled and its potential contribution to precoding in a multiuser MIMO (MU-MIMO) downlink. Unlike SNR- or SINR-based thresholding methods, the proposed approach uses rich spatial channel statistics and learns nontrivial correlation effects that affect eventual MU-MIMO scheduling decisions. To train the self-nomination network under an average feedback constraint, we propose two different strategies: one based on direct optimization with gradient approximations, and another using policy gradient-based optimization with a stochastic Bernoulli policy to handle non-differentiable scheduling. The framework also supports proportional-fair scheduling by incorporating dynamic user weights. Numerical results confirm that the proposed self-nomination method significantly reduces CSI feedback overhead. Compared to baseline feedback methods, self-nomination can reduce feedback by as much as 65%, saving not only bandwidth but also allowing many UEs to avoid feedback altogether (and thus, potentially enter a sleep mode). Self-nomination achieves this significant savings with negligible reduction in sum-rate or fairness.

This paper proposes a novel mixed panel architecture for channel state information (CSI) acquisition from uplink (UL) channel training in hybrid analog-digital beamforming systems with partially-connected structures in time-division duplex massive multiple-input multiple-output networks. The proposed architecture combines a few fully-digital (FD) panels with a large number of subarray-based panels for UL pilot reception, addressing CSI acquisition challenges while balancing performance and power efficiency. We then develop a unified method that can utilize measurements from both panel types to estimate the required CSI for analog precoder design. In particular, by recognizing that the dominant eigenvector of the panel covariance matrix is crucial for analog precoding, we propose an orthogonal matching pursuit-type algorithm to estimate it by exploiting channel sparsity in the angular domain. Additionally, we introduce a data-driven technique to optimize analog combiners for subarray-based panels during UL pilot reception. Numerical experiments demonstrate that our proposed method approaches the performance of an all-FD-panel architecture for UL pilot training while maintaining the low complexity of all-subarray-based-panel structures

Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success is defined by end-to-end distortion rather than traditional metrics such as block error rate. Previous studies have shown significant improvements achieved through deep learning (DL)-driven JSCC compared to traditional separate source and channel coding. However, JSCC is impractical in existing communication networks, where application and network providers are typically different entities connected over general-purpose TCP/IP links. In this paper, we propose designing the source and channel codes separately and sequentially via a novel multi-level reliability interface. This conceptual interface enables JSCC at both the learned source and channel mappers and achieves many of the gains observed in existing DL-based JSCC work (which would require a fully joint design between the application and the network), such as lower end-to-end distortion and graceful degradation of distortion with channel quality. We believe this work represents an important step towards realizing semantic communications in wireless networks.

Industry and academia have been working towards the evolution from Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many cells, each served by a single Base Station equipped with several antennas, the whole coverage area is jointly covered by several Access Points (AP) equipped with few or single antennas. Nevertheless, when choosing between deploying more APs with few or single antennas or fewer APs equipped with many antennas, one observes an inherent trade-off between the beamforming and macro-diversity gains that has not been investigated in the literature. Given a total number of antenna elements and total downlink power, under a channel model that takes into account a probability of Line-of-Sight (LoS) as a function of the distance between the User Equipments (UEs) and APs, our numerical results show that there exists a “sweet spot” on the optimal number of APs and of antenna elements per AP which is a function of the physical dimensions of the coverage area.

Outdoor-to-indoor signal propagation poses significant challenges to millimeter-wave link budgets. To gain insight into outdoor-to-indoor millimeter-wave at 28GHz, we conducted an extensive measurement campaign consisting of over 2,200 link measurements in West Harlem, New York City, covering seven highly diverse buildings. A path loss model constructed over all measured links shows an average of 30dB excess loss over free space at distances beyond 50m. We find the type of glass to be the dominant factor in outdoor-to-indoor loss, with 20dB observed difference between grouped scenarios with low-and high-loss glass. Other factors such as the presence of scaffolding, tree foliage, or elevated subway tracks, as well as difference in floor height are also found to have a 5–10dB impact. We show that for urban buildings with high-loss glass, outdoor-to-indoor downlink capacity up to 400Mb/s is supported for 90% of indoor customer premises equipment by a base station up to 40m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, downlink capacity over 2.8/1.4Gb/s is possible from a base station 57/133m away within line-of-sight. We expect these results to help inform the planning of millimeter-wave networks targeting outdoor-to-indoor deployments in dense urban environments, as well as provide insight into the development of scheduling and beam management algorithms.

Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit theory approach with frequency-selective scattering parameters, we propose a general framework for optimizing the MISO achievable rate that incorporates Bode-Fano wideband matching theory. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We apply this framework to a single electric Chu’s antenna and an array of dipole antennas. We compare the optimized achievable rate obtained numerically with other benchmarks like the ideal achievable rate computed by disregarding matching constraints and the achievable rate obtained by using sub-optimal matching strategies like conjugate matching and frequency-flat transmission. We also propose a practical methodology to approximate the achievable rate bound by using the optimal transmission coefficient to derive a physically realizable matching network through the ADS software.

Conventional achievable rate analysis using Shannon's theory does not assume practical constraints imposed by Bode-Fano wideband matching theory. This leads to an achievable rate bound that cannot be attained by practical matching networks. In this paper, we generalize the information-theoretic achievable rate of a single-input-single-output (SISO) system by incorporating wideband matching constraints at the transmitter. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We also propose a practical strategy to design a physically realizable matching network through the ADS software which attains the achievable rate bound with near-optimality. In simulations, we apply this framework to a Chu's antenna and compare the achievable rate performance with the conventional conjugate matching strategy.

Downlink massive multiple-input multiple-output (MIMO) precoding algorithms in frequency division duplexing (FDD) systems rely on accurate channel state information (CSI) feedback from users. In this paper, we analyze the tradeoff between the CSI feedback overhead and the performance achieved by the users in systems in terms of achievable rate. The final goal of the proposed system is to determine the beamforming information (i.e., precoding) from channel realizations. We employ a deep learning-based approach to design the end-to-end precoding-oriented feedback architecture, that includes learned pilots, users' compressors, and base station processing. We propose a loss function that maximizes the sum of achievable rates with minimal feedback overhead. Simulation results show that our approach outperforms previous precoding-oriented methods, and provides more efficient solutions with respect to conventional methods that separate the CSI compression blocks from the precoding processing.

In this paper, a communication and control codesign framework is presented based on survival time, i.e., the time that a closed-loop wireless control system can continue without an anticipated message. The goal is to ensure the stability of wireless control systems with minimal resource usage. A novel interface between the controller and the scheduler is proposed, where the key communication and control parameters are analyzed for co-design, and jointly optimized. The proposed co-design framework leverages link adaptation for the communications system and sampling period adaptation for the closed-loop control system to preserve more resources. Our numerical example on closed-loop velocity control demonstrates a pronounced reduction of resources needed for control stability in contrast to the separate design paradigm that requires ultrahigh link reliability. An additional 52% reduction in resource utilization is achieved by further adapting the key parameters when the system is in survival mode.

The surge of massive antenna arrays in wireless networks calls for the adoption of analog/hybrid array solutions, where multiple antenna elements are driven by a common radio front end to form a beam along a specific angle in order to maximize the beamforming gain. Many heuristics have been proposed to sample the angular domain by trading off between sampling step size and overhead, where arbitrarily small angular step size is only attainable with infinite sampling overhead. We show that, for uniform linear and rectangular arrays, lossless reconstruction of the array’s angular responses at arbitrary angular precision is possible using a finite number of samples without resorting to assumptions of angular sparsity. The proposed method, (SARA), defines how many and which angles to be sampled and the corresponding reconstruction. This general solution to scan the angular domain can therefore be applied not only to beam acquisition and channel estimation, but also to radio imaging techniques, making it a candidate for future integrated sensing and communications (ISAC). Extensive simulation results for target detection and radio imaging have demonstrated clear advantages of SARA over other considered baselines, both in terms of angular reconstruction performance and computational complexity.

Millimeter-Wave (mmWave) has great potential to provide high data dates given its large available bandwidth, but its severe path loss and high propagation sensitivity to different environmental conditions make deployment planning particularly challenging. Traditional slope-intercept models fall short in capturing large site-specific variations due to urban clutter, terrain tilt or foliage, and ray-tracing faces challenges in characterizing mmWave propagation accurately with reasonable complexity. In this work, we apply machine learning (ML) techniques to predict mmWave path loss on a link-to-link basis over an extensive set of 28 GHz field measurements collected in a major city of USA, with over 120,000 links from both urban and suburban scenarios, with over 40 dB variation for links at similar distances. Either raw environmental profile (terrain+clutter) of each link or 8 selected expert features are used to either directly predict path loss via regression-based approaches or predict the best performing option out of a pool of theoretical/empirical propagation models. Our evaluation shows that Lasso regression provides the best path loss prediction with a performance (RMSE 8.1 dB) comparable to the per-site slope-intercept fit (RMSE 8.0 dB), whereas model selection method achieves 8.6 dB RMSE, both are significantly better than the best a posteriori 3GPP model (UMa-NLOS, 10.0 dB).

In the US, people spend 87% of their time indoors and have an average of four connected devices per person (in 2020). As such, providing indoor coverage has always been a challenge but becomes even more difficult as carrier frequencies increase to mmWave and beyond. This paper investigates the outdoor and outdoor-indoor coverage of an urban network comparing globally standardized building penetration models and implementing models to corresponding scenarios. The glass used in windows of buildings in the grid plays a pivotal role in determining the outdoor-to-indoor propagation loss. For 28 GHz with 1 W/polarization transmit power in the urban street grid, the downlink data rates for 90% of outdoor users are estimated at over 250 Mbps. In contrast, 15% of indoor users are estimated to be in outage, with SNR <−3 dB when base stations are 400 m apart with one-fifth of the buildings imposing high penetration loss (∼ 35 dB). At 3.5 GHz, base stations may achieve over 250 Mbps for 90% indoor users if 400 MHz bandwidth with 100 W/polarization transmit power is available. The methods and models presented can be used to facilitate decisions regarding the density and transmit power required to provide high data rates to majority users in urban centers.

Bit-interleaved coded modulation (BICM) and multilevel coded modulation (MLC) are commonly used to combine polar codes with high order modulation. While BICM benefits from simple design and the separation of coding and modulation, MLC shows better performance under successive-cancellation de-coding. In this paper we propose a hybrid polar coded modulation scheme that lies between BICM and MLC, wherein a fraction of bits are assigned to set-partition (SP) labeling and the remaining bits are assigned for Gray labeling. The SP labeled bits undergo sequential demodulation, using iterative demodulation and polar decoding similar to MLC, whereas the Gray labeled bits are first demodulated in parallel and then sent for decoding similar to BICM. Either polar codes or other channel codes (such as LDPC codes) can be used for the Gray labeled bits. For length 2048 rate 1/2 polar code on 256-QAM, the performance gap be-tween BICM (Gray labeling only) and MLC (SP labeling only) can be almost fully closed by the hybrid scheme. Notably, the hybrid scheme has a significant latency advantage over MLC. These performance gains make the proposed scheme attractive for future communication systems such as 6G.

Outdoor-to-indoor (OtI) signal propagation further challenges link budgets at millimeter-wave (mmWave). To gain insight into OtI mmWave at 28 GHz, we conducted an extensive measurement campaign consisting of over 2,000 link measurements in West Harlem, New York City, covering seven highly diverse buildings. A path loss model constructed over all links shows an average of 30 dB excess loss over free space at distances beyond 50 m. We find the type of glass to be the dominant factor in OtI loss, with 20 dB observed difference between clustered scenarios with low- and high-loss glass. Other factors, such as difference in floor height, are found to have an impact between 5--10 dB. We show that for urban buildings with high-loss glass, OtI data rates up to 400 Mb/s are supported for 90% of indoor users by a base station (BS) up to 49 m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, data rates over 2.8/1.4 Gb/s are possible from a BS 68/175 m away when a line-of-sight path is available. We expect these results to be useful for the deployment of OtI mmWave networks in dense urban environments and the development of scheduling and beam management algorithms.

Given a large wireless network consisting of randomly deployed nodes, where each of the nodes wants to transmit to a random destination node within the network at some equal rate, how fast can the sum rate grow as the number of nodes scales up at fixed density? This question is important because it captures the bottleneck of message exchanging among randomly deployed Internet-of-Things (IoT) devices, and it models nicely the wireless backhaul communication among access points or within airborne communication systems. Previous work has shown that, given an extended network with fixed density, multihop routing based approach provides sum rate that scales at most as the square root of network size, where as hierarchical cooperation protocols have the potential to support linear scaling. With limited power, the SNR decreases at least inverse proportional to the network size, and therefore the benefit of hierarchical cooperation will be curbed by the combined effects of path loss and local communication cost. We show in this paper how the path loss and local cooperation cost reshape the capacity scaling law results.

Delivering Gbps high user rate over long distances (~1 km) is challenging, and the abundant spectrum available in millimeter wave band cannot solve the challenge by its own due to the severe path loss and other limitations. Since it is economically challenging to deploy wired backhaul every few hundred meters, relays (e.g., wireless access points) have been proposed to extend the coverage of a base station, which has wired connection to the core network. These relays, deployed every few hundred meters, serve the users in their vicinity and are backhauled to the base station through wireless connections. In this paper, the wireless-relayed backhaul design has been formulated as a topology-bandwidth-power joint optimization problem, and the influence of path loss, angular spread, array size, and RF power limitation on the user rate has been evaluated. It has been shown that for a linear network deployed along the street at 28 GHz, when high joint directional gain (50 dBi) is available, 1 Gb/s user rate within cell range of 1 km can be delivered using 1.5 GHz of bandwidth (using single polarization antennas). The user rates drop precipitously when joint directional gain is reduced, or when the path loss is much more severe. When the number of RF chains is limited, the benefit of larger arrays will eventually be surpassed by the increased channel estimation penalty as the effective beamforming gain saturates owing to the channel angular spread.

Great increase in wireless access rates might be attainable using the large amount of spectrum available in the millimeter wave (mmWave) band. However, higher propagation losses inherent in these frequencies must be addressed, especially at ranges beyond 100 m and in non-line-of-sight (NLOS) settings. In contrast to the interference limited legacy cellular systems, where using more bandwidth is favorable, to use wider bandwidth for mmWave channels in noise limited settings may be ineffective or even counterproductive when accounting for channel estimation penalty. In this paper, we quantify the maximum beneficial bandwidth for mmWave transmission in some typical deployment scenarios where pilot-based channel estimation penalty is considered assuming a minimum mean square error channel estimator at the receiver. We find that, under I.I.D. block fading model with coherence time Tcand coherence bandwidth Bc, for transmitters and receivers equipped with a single antenna, the optimal (rate maximizing) signal-to-noise-ratio is a constant that only depends on the product BcTc, which measures the channel coherence and equals the average number of orthogonal symbols per each independent channel coefficient. That is, for fixed channel coherence BcTc, the optimal bandwidth scales linearly with the received signal power. Under 3GPP urban micro NLOS path loss model with coherence time Tc= 5 ms and coherence bandwidth Bc= 10 MHz, using 52-dBm equivalent isotropic radiated power (EIRP) at the transmitter and 11-dBi antenna gain at the receiver, the maximum beneficial bandwidth at 28 (resp. 39) GHz is less than 1 GHz at a distance beyond 210 (resp. 170) m with maximum throughput about 200 Mbps, and less than 100 MHz beyond 400 (resp. 310) m with maximum throughput about 20 Mbps. At EIRP of 85 dBm, corresponding to the FCC limit of 75 dBm per 100 MHz, 1 Gbps rate can be delivered using 1-GHz bandwidth up to 860 (resp. 680) m.

In non-coherent wideband fading channels, where energy rather than spectrum is the limiting resource, peaky and non-peaky signaling schemes have long been considered species apart, as the first approaches asymptotically the capacity of a wideband AWGN channel with the same average SNR, whereas the second reaches a peak rate at some finite critical bandwidth and then falls to zero as bandwidth grows to infinity. In this paper, it is shown that this distinction is in fact an artifact of the limited attention paid in the past to the product between the bandwidth and the fraction of time it is in use. This fundamental quantity, called bandwidth occupancy, measures average bandwidth usage over time. For all signaling schemes with the same bandwidth occupancy, achievable rates approach to the wideband AWGN capacity within the same gap as the bandwidth occupancy approaches its critical value, and decrease to zero as the occupancy goes to infinity. This unified analysis produces quantitative closed-form expressions for the ideal bandwidth occupancy, recovers the existing capacity results for (non-) peaky signaling schemes, and unveils a tradeoff between the accuracy of approximating capacity with a generalized Taylor polynomial and the accuracy with which the optimal bandwidth occupancy can be bounded.

Hierarchical cooperation schemes in wireless networks rely on local cooperation among neighboring nodes to create virtual multiple-input multiple-output (MIMO) connections between clusters of nodes. It was shown that, by applying the virtual MIMO technique recursively in a hierarchical manner, the sum rate of all source-destination pairs can scale linearly with the number of nodes in the network. In this paper we focus on the impact of local cooperation and establish new capacity scaling bounds for the virtual MIMO transmission taking into account the constraints of local communication both at the transmitters and the receivers. We show that the cost of local communication, which is inevitable to establish the virtual MIMO transmission, grows exponentially with the number of layers in the cooperation hierarchy and plays a vital role in determining the overall performance of the hierarchical virtual MIMO cooperation.

The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper (respectively, lower) bounding models, which can be used to calculate the outer (respectively, inner) bounds for the capacity region of the original noisy network. Based on the network equivalence framework, this paper presents scalable upper and lower bounding models for wireless networks with potentially many nodes. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels and broadcast channels. The upper bounding model, consisting of only point-to-point bit pipes, is constructed by first extending the one-shot upper bounding models developed by Calmon et al. and then integrating them with network equivalence tools. The lower bounding model, consisting of both point-to-point and point-to-points bit pipes, is constructed based on a two-step update of the lower bounding models to incorporate the broadcast nature of wireless transmission. The main advantages of the proposed methods are their simplicity and the fact that they can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper and lower bounds can approach the capacity in some setups.

Data transmission over multiple-hop networks is impaired by random deleterious events, and characterizing the probability of error for the end-to-end transmission is challenging as the size of networks grows. Adams et al. showed that, when re-encoding at intermediate nodes is enabled, coded transmission over tandem/parallel links can be reduced to a single equivalent link with a specified probability function. Although iterative application of the tandem/parallel reduction techniques in alternation can simplify the task, they are generally not sufficient to reduce an arbitrary network to a single link. In this paper, we propose upper- and lower- bounding processes to bound the end-to-end probability distribution of a network by combining the parallel/tandem link reduction with the structure of flows over the network. We evaluate the performance of the proposed bounding methods at the 99% success rate of end-to-end data transmission over randomly generated acyclic networks. The numerical results demonstrate that our bounding approaches enable us to characterize a network by a single probability function to a very good precision.

Peaky and non-peaky signaling schemes have long been considered species apart in non-coherent wideband fading channels, as the first approaches asymptotically the linear-in-power capacity of a wideband AWGN channel with the same SNR, whereas the second reaches a nearly power-limited peak rate at some finite critical bandwidth and then falls to zero as bandwidth grows to infinity. In this paper it is shown that this distinction is in fact an artifact of the limited attention paid in the past to the product between the bandwidth and the fraction of time it is in use. This fundamental quantity, that is termed bandwidth occupancy, measures average bandwidth usage over time. The two types of signaling in the literature are harmonized to show that, for any type of signals, there is a fundamental limit-a critical bandwidth occupancy. All signaling schemes with the same bandwidth occupancy approach the capacity of wideband AWGN channels with the same asymptotic behavior as the bandwidth occupancy grows to its critical value. For a bandwidth occupancy above the critical, rate decreases to zero as the bandwidth occupancy goes to infinity.

Indoor communications in the 60 GHz band is capable of supporting multi-gigabit wireless access thanks to the abundant spectrum and the possibility of using dense antenna arrays. However, the high directivity and penetration loss make it vulnerable to blockage events, which can be frequent in indoor environments. Given network topology information in sufficient precision, we investigate the average throughput and outage probability when the connection between any two nodes can be established either via the line-of-sight (LOS) link, through a reflection link, or by a half-duplex relay node. We model the reflection link as an LOS with extra power loss and derive the closed-form expression for the relative reflection loss. For networks with a central coordinator and multiple relays, we also propose a generic algorithm, maximum throughput path selection (MTPS), to select the optimal path that maximizes the throughput. The complexity of the MTPS algorithm is O(n2) for networks equipped with n relays, whereas a brute-forced algorithm has complexity of O(n · n!). Numerical results show that increasing the number of relays can significantly increase the average throughput and decrease the outage probability, and resorting to reflection paths provides significant gains when the probability of link blockage is high.

We investigate the performance of delay constrained data transmission over wireless networks without end-to-end feedback. Forward error-correction coding (FEC) is performed at the bit level to combat channel distortions and random linear network coding (RLNC) is performed at the packet level to recover from packet erasures. We focus on the scenario where RLNC re-encoding is performed at intermediate nodes and we assume that any packet that contains bit errors after FEC decoding can be detected and erased. To facilitate explicit characterization of data transmission over network-coded wireless systems, we propose a generic two-layer abstraction of a network that models both bit/symbol-level operations at the lower layer (termed PHY-layer) over several heterogeneous links and packet-level operations at the upper layer (termed NET-layer). Based on this model, we propose a network reduction method to characterize the throughput-reliability function of the end-to-end transmission. Our approach not only reveals an explicit tradeoff between data delivery rate and reliability, but also provides an intuitive visualization of the bottlenecks within the underlying network. We illustrate our approach via a point-to-point link and a relay network and highlight the advantages of this method over capacity-based approaches.

The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper/lower bounding models for the original noisy network. This paper presents scalable upper bounding models for wireless networks, by firstly extending the “one-shot” bounding models developed by Calmon et al. and then integrating them with network equivalence tools. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels (MACs) and broadcast channels (BCs). The main advantages of the proposed method is its simplicity and the fact that it can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper bounds can approach the capacity in some setups.

Motivated by the framework of network equivalence theory [1], [2], we present capacity lower bounding models for wireless networks by construction of noiseless networks which can be used to calculate an inner bound for the corresponding wireless network. We first extend the “one-shot” lower bounding model [6] to many-user scenarios, and then propose a two-step update of the one-shot models to incorporate the broadcast nature of wireless transmission. The main advantage of the proposed lower bounding method is its simplicity and the fact that it can be easily extended to larger networks. We demonstrate by examples that the resulting lower bounds can even approach the capacity in some setups.

We investigate capacity bounds for a wireless multicast relay network where two sources simultaneously multicast to two destinations with the help of a full-duplex relay node. The two sources and the relay use the same channel resources (i.e. co-channel transmission). We assume Gaussian channels with time-invariant channel gains which are known by all nodes. The two source nodes are connected by orthogonal limited-rate error-free conferencing links. By extending the proof of the converse for the Gaussian relay channel and introducing two lemmas on conditional (co-)variance, we present two genie-aided outer bounds of the capacity region for this multicast relay network. We extend noisy network coding to use source cooperation with the help of the theory of network equivalence. We also propose a new coding scheme, partial-decode-and-forward based linear network coding, which is essentially a hybrid scheme utilizing rate-splitting and messages conferencing at the source nodes, partial decoding and linear network coding at the relay, and joint decoding at each destination. A low-complexity alternative scheme, analog network coding based on amplify-and-forward relaying, is also investigated and shown to benefit greatly from the help of the conferencing links and can even outperform noisy network coding when the coherent combining gain is dominant.

Noisy network coding (NNC) has been shown to outperform standard compress-and-forward (CF) in networks with multiple relays and/or multiple destinations. Recently, short-message noisy network coding (SNNC) has been proved to achieve the same rate region as NNC for independent sources but with significantly reduced encoding delay and decoding complexity. In this paper, we show that when partial cooperation between source nodes is possible, by performing rate-splitting, message exchange, and superposition coding with proper power allocation at the source nodes, SNNC can achieve a strictly larger rate region than NNC. The gain comes from coherent combining at all the receiving nodes.

Orthogonal frequency division multiplexing (OFDM) technique has gained increasing popularity in both wired and wireless communication systems. However, in the conventional OFDM systems the insertion of a cyclic prefix (CP) and the transmission of periodic training sequences for purpose of channel estimation decrease the system's spectral efficiency. As an alternative to OFDM, isotropic orthogonal transform algorithm (IOTA)-based multicarrier system adopts a proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. In this study, the authors propose linearly precoded IOTA-based multicarrier systems to achieve blind channel estimation by utilising the structure of auto-correlation and cross-correlation matrices introduced by precoding. The results show that the proposed IOTA-based multicarrier systems achieve better power and spectral efficiency compared with the conventional OFDM systems.

We investigate the capacity bounds for a wireless multicast relay network where two sources simultaneously multicast to two destinations through Gaussian channels with the help of a full-duplex relay node. All the individual channel gains are assumed to be time-invariant and known to every nodes in the network. The transmissions from two sources and from the relay use the same channel resource (i.e. co-channel transmission) and the two source nodes are connected with an orthogonal error-free backhaul. This multicast relay network is generic in the sense that it can be extended to more general networks by tuning the channel gains within the range [0, ∞). By extending the proof of the converse developed by Cover and El Gamal for the Gaussian relay channel, we characterize the cut-set bound for this multicast relay network. We also present a lower bound by using decoding-and-forward relaying combined with network beam-forming.

In this article, we consider practical approaches to Costa precoding (also known as dirty paper coding). Specifically, we propose a symbol-by-symbol scheme for cancellation of interference known at the transmitter in a relay-aided downlink channel. For finite-alphabet signaling and interference, we derive the optimal (in terms of maximum mutual information) modulator under a given power constraint. A sub-optimal modulator is also proposed by formulating an optimization problem that maximizes the minimum distance of the signal constellation, and this non-convex optimization problem is approximately solved by semi-definite relaxation. For the case of binary signaling with binary interference, we obtain a closed-form solution for the sub-optimal modulator, which only suffers little performance degradation compared to the optimal modulator in the region of interest. For more general signal constellations and more general interference distributions, we propose an optimized Tomlinson-Harashima precoder (THP), which uniformly outperforms conventional THP with heuristic parameters. Bit-level simulation shows that the optimal and sub-optimal modulators can achieve significant gains over the THP benchmark as well as over non-Costa reference schemes, especially when the power of the interference is larger than the power of the noise.

We investigate cooperative network coding strategies for relay-aided two-source two-destination wireless networks with a backhaul connection between the source nodes. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding schemes, namely, finite field and linear network coding, and lattice coding. To further exploit the backhaul connection, we also propose network coding based beamforming. We measure the performance in term of achievable rates over Gaussian channels, and observe significant gains over benchmark schemes. We derive the achievable rate regions for these schemes and find the cut-set bound for our system. We also show that the cut-set bound can be achieved by network coding based beamforming when the signal-to-noise ratios lie in the sphere defined by the source-relay and relay-destination channel gains.

We investigate cooperative strategies for relay-aided multi-source multi-destination wireless networks with backhaul support. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding (NC) schemes, namely, finite field NC (FNC), linear NC (LNC), and lattice coding. To further exploit the backhaul connection, we also propose NC-based beam-forming (NBF). We measure the performance in term of achievable rates over Gaussian channels and observe significant gains over a benchmark scheme. The benefit of using backhaul is also clearly demonstrated in most of scenarios.

OFDM/OQAM has been considered as an attractive alternative to classic OFDM with cyclic prefix (CP) over doubly dispersive channels. By utilising well designed pulse shapes and removing CP, OFDM/OQAM has the advantage of reduced out- of-band energy and a theoretically higher spectral efficiency. However, channel estimation over doubly dispersive channels has been a big problem for OFDM/OQAM due to the non- orthogonality between the real and imaginary parts of its modulated signals. Therefore conventional channel estimation (CE) methods used for OFDM cannot be directly applied to OFDM/OQAM. Recently a preamble-based CE method - interference approximation method (IAM) - has been proposed to ease this task. By treating the intrinsic interference from neighbour symbols as known information, two heuristic preamble sequences have been constructed based on tentative observations, which turn out to be suboptimal. In this paper, we present a general theoretical framework for IAM preamble design and apply it to identify the optimal IAM preamble sequence which results in a higher gain. Numerical results have verified the effectiveness of the theoretical framework and a gain of 2.4 dB against CP- OFDM has been demonstrated with the new preamble in various doubly dispersive channels with a QPSK modulation.

We design an optimum modulator for the Costa (dirty-paper) precoding problem under the constraint of a binary signaling alphabet, and assuming the interference symbols belong to a binary constellation. We evaluate the performance of our technique in terms of the mutual information between the channel input and output, and compare it to that of Tomlinson-Harashima precoding (THP) with optimized parameters. We show that our optimal modulator is always better than THP. In many relevant scenarios, the performance difference is significant