5G wireless networks are expected to usher in massive Machine Type Communications (mMTC) pertaining to the Internet of Things (IoT). A large fraction of the devices participating in IoT are likely to be low-power devices that generate sporadic traffic. It is anticipated that autonomous grant-free communications would be needed to accommodate these devices and the traffic they generate. Preamble transmission is a key part of such grant-free communications, and system performance including the desired power savings for the devices depends critically on the preamble detection-false alarm characteristics. In this context, low-power devices in extended coverage scenarios present a stiff challenge to system designers. In this study, we show that with narrowband resource allocations, use of preamble acknowledgements and judiciously set power control targets one can achieve the desired performance even for low-power devices in challenging scenarios.

Throughput performance of WiFi networks, which employ the Carrier Sense Multiple Access (CSMA) technique, can be estimated fairly accurately using Bianchi's analytic solution provided all elements of the network are within the carrier sensing range of one another. However, this solution cannot be applied to networks that are spread over a wide area. A paper by Liew, et al proposed an interesting approximate solution for such networks. While this solution was shown to work well in a variety of conditions, it suffers from a limitation in that it requires all transmitter-receiver pairs to have independent and identically distributed (i.i.d.) transmission times. In this paper, we generalize some of the key results of Liew, et al by removing the restriction on transmission times, which leads to a solution technique that can be used to analyze CSMA networks where different transmitter-receiver pairs operate at different bit rates. We also provide a few examples to illustrate how well this technique works in different settings and compare it with solutions based on other methods.

In this paper, we present a practical method to construct dirty paper coding (DPC) schemes using sum codes. Unlike the commonly used approach to DPC where the coding scheme involves concatenation of a channel code and a quantization code, the proposed method embodies a unified approach that emulates the binning method used in the proof of the DPC result. Auxiliary bits are used to create the desired number of code vectors in each bin. Sum codes are obtained when information sequences augmented with auxiliary bits are encoded using linear block codes. Sum-code-based DPC schemes can be implemented using any linear block code, and entail a relatively small increase in decoder complexity when compared to standard communication schemes. They can also lead to significant reduction in transmit power in comparison to standard schemes.

Base station cooperation has been shown to significantly mitigate the interference perceived in cellular networks and improve the quality of user experience. On the uplink, joint processing (JP) across antennas within a cluster of base stations yields significant gain, but requires significant backhaul to transport complex I & Q signals. The Network Interference Cancellation Engine (NICE) can yield throughput gains similar to that of JP, but with one to two orders of magnitude less backhaul. While NICE demonstrates significant potential for cellular networks, its performance has been evaluated under somewhat idealistic conditions. For instance, full buffer traffic has generally been assumed, thereby leading to a fully loaded system with strong interference. Bursty traffic may result in a partially loaded system with less interference and impact the gains of interference-mitigating techniques such as NICE. In addition, an ideal backhaul with negligible delay has generally been assumed. Delays in information exchanges between base stations may decrease the efficacy of base station cooperation. In this paper, the performance of NICE is evaluated under non full buffer traffic and non-ideal backhaul.

In this paper, we present a practical Dirty Paper Coding (DPC) scheme using sum codes based on LDPC codes. While a typical sum-code-based DPC scheme uses a constrained decoder as part of the encoding operation, such an approach fails in the case of LDPC codes since the constrained decoder based on a standard LDPC decoder tends to get stuck at highly suboptimal points in typical scenarios. We get around this difficulty via a simple code construction method based on bit-mapping or permutation. Examples included in this paper show that compared to standard communication schemes the proposed DPC scheme yields substantial savings in terms of the transmit energy required to attain desired error performance.

While the information theoretic result commonly referred to as “dirty paper coding” [1] has been known for long, it has not led to practical schemes that can be implemented easily in real-life communication systems. In this paper, we present the concept of sum codes and show how they provide a convenient platform for dirty paper coding. Comb-combined sum codes, which provide a convenient method of sum-code construction, are particularly useful in this respect. One can build comb-combined sum codes based on any linear block code to achieve reduction in the transmit energy required to attain a desired performance. Using an example based on tail-biting convolutional codes, we show how practical dirty paper coding may be implemented using any convenient linear block code.

Although the information theoretic result referred to as “dirty paper coding” [1] has been known for quite a while, it has not led to schemes that look attractive enough for real-life communication systems. In a companion paper [2], we presented the concept of sum codes and showed how they provide a convenient platform for interference suppression via dirty paper coding. In this paper, we present a dirty paper coding scheme based on trellis-coded modulation, which could be looked upon as an alternative to the sum-codes-based approach. Since trellis-coded modulation is designed for multi-level signal constellations, it is expected to work well as a platform for the proposed dirty paper coding scheme in which multi-level signal constellations play a critical role.

It is widely believed that cooperation among base stations can be employed to enhance the interference mitigation capability of base station receivers in cellular networks. Among the base station cooperation schemes that have been proposed to address the uplink interference problem, two promising ones are 1) Joint Multi-Cell MMSE Processing also referred to as Network MIMO; and 2) Multi-Cell Successive Interference Cancellation (MC-SIC). While both of these methods can substantially improve the performance of cellular networks, they have complementary strengths and limitations when implemented in a practical setting. In this paper, we propose a hybrid scheme called Generalized Network Interference Cancellation Engine, which combines the strengths of these two approaches while working within the constraints imposed by practical implementations. This view is borne out by the performance results presented in this paper.

The demand for high-rate wireless services grows unabated as new mobile devices and data-hungry applications continue to emerge. However, despite many advanced physical and link layer techniques that are employed in today's cellular networks, a large number of mobile users, especially at the cell edges, receive inadequate data rates due to high interference and path loss. Several techniques that exploit inter-base station cooperation have been proposed to address the inter-cell interference problem. While the initial studies show promising gains in idealized environments, it is important to analyze these techniques in a more realistic setting. This study provides detailed analyses of the benefits provided by uplink base station cooperation with several practical constraints and issues that would be encountered in a realistic cellular network deployment. Our results indicate that the cooperative signal processing techniques are robust to channel estimation errors given the pilot structure available in contemporary wireless systems. Furthermore, uplink cooperation can substantially improve the interference management capability of the cellular network, providing significant rate improvement over current non-cooperative networks.

Heterogeneous networks comprising overlapping layers of macro cells and small cells are expected to be widely deployed to cope with the rapidly increasing demand for wireless bandwidth. While judicious resource sharing and interference management mechanisms allow these networks to provide adequate performance for static or pedestrian users, they often fail to meet the needs of highly mobile users. In this paper, we propose a cooperative data transmission scheme called "Seamless Macro-Cell-Anchored Radio Transmission" that enables even highly mobile users to experience performance levels similar to those enjoyed by static users. The performance improvements effected by this scheme occur mainly at low percentiles; in that sense it can be said to deliver sustained end-user experience even to highly mobile users. Further improvement in edge performance is possible with the use of rate thresholds in conjunction with the proposed scheme.

Recently, there has been considerable interest in the potential of inter-base station cooperation techniques to manage excessive out-of-cell interference in evolving cellular networks. It has been observed that on the uplink, significant improvements in interference rejection can be attained by exchanging complex-valued received signal samples across a cluster of cells and employing joint multi-antenna receiver processing. However, such processing incurs substantial increases in backhaul overhead, exceeding the actual information delivered by at least an order of magnitude. In this paper, we propose and describe a signal processing approach based on a novel Network Interference Cancellation Engine (NICE), which opportunistically cancels out-of-cell interference by exchanging decoded dominant interferer data among cooperating cells and reconstructing interfering signals. Simulation results with NICE processing in cellular networks show that substantial improvements (5-7 dB) in signal-to-interference-plus-noise ratio may be achieved over current non-cooperative multi-antenna receiver processing techniques while allowing backhaul overhead to be reduced by factors on the order of 40 to 50 relative to joint processing techniques employing the exchange of complex-valued received signal samples.

Single frequency networks (SFN) in combination with a multi-carrier transmission scheme such as OFDM are often proposed as a means to provide continuous coverage of broadcast applications over a large area. In this paper, we propose a distributed beam-forming algorithm that makes use of channel state information fed back by receivers to improve the performance of the weaker connections. The distributed nature of the proposed algorithm makes it easier to implement when compared to its centralized counterparts. Simulation studies show that the proposed algorithm results in a significant improvement in the performance of SFNs supporting broadcast or multicast applications.

In this paper, the feasibility and capacity benefits of deploying low-cost relay nodes with in-band wireless backhaul capability in cellular networks are studied. Our proposed scheme is similar to a cell-splitting architecture where the base station density is increased, thus reducing the effective cell size. Cell-splitting in an interference-limited deployment provides capacity gains that are proportional to the number of additional base stations deployed, but at significant cost driven primarily by wired backhaul requirements. The use of in-band wireless backhaul may significantly reduce the cost, but sharing bandwidth with the backhaul results in reduced access rates relative to that of cell-splitting with wired backhaul. Further cost savings may be achieved with the use of low power, low complexity relays in lieu of the additional base stations. In this paper, we show that a cellular architecture with base stations supplemented by simple decode-and-forward relays can provide significant capacity improvements despite the overhead associated with in-band backhaul.

Single frequency networks (SFN) in combination with a multi-carrier transmission scheme such as OFDM are often proposed as a means to provide continuous coverage of broadcast applications over a large area. While this combination does succeed in providing continuous coverage, it has certain limitations. Specifically, at points where signals from multiple base-stations are received, the overall signal-to-noise ratio can be poor because of relative phase differences. In this paper, we show how, through the use of orthogonal space-time codes, this limitation of SFNs can be overcome to improve their overall performance. This scheme could be viewed as an ldquoopen-looprdquo implementation of multi-cell cooperative transmission.

In this paper, we investigate joint resource allocation and routing for a wireless mesh network consisting of fixed access points inter-connected through multi-channel wireless links. Some of the mesh routers are assumed to function as gateways with high bandwidth connections to a wired network and the mesh network is assumed to provide multi-hop capability where the traffic entering the mesh via gateways may be carried over multiple wireless hops towards the destination mesh routers. The main objective of this work is to optimize resource allocation and routing in such networks; in this regard, we study a cross- layer optimization problem that includes power control, channel allocation, link scheduling and routing. We provide results highlighting the capacity benefits of our proposed approach; these results indicate that significant throughput improvements can be achieved with cross-layer system design.

We investigate optimum zero-forcing beamforming in multiple antenna broadcast channels with per-antenna power constraints. We show that standard zero-forcing techniques, such as the Moore-Penrose pseudo-inverse, considered mainly in the context of sum-power constrained systems are suboptimal when there are per-antenna power constraints. We formulate convex optimization problems to find the optimum zero-forcing beamforming vectors. Our results indicate that optimizing the antenna outputs based on the per-antenna constraints may improve the rate considerably when the number of transmit antennas is larger the number of receive antennas. Having more transmit antennas gives rise to additional signal space dimensions that may be exploited effectively to reduce transmit power at particular antennas with limited power budget.

In this paper, we propose a cross layer optimization framework for multi-hop routing and resource allocation design in an orthogonal frequency division multiple access (OFDMA) based wireless mesh network. The network under consideration is assumed to consist of fixed mesh routers (or base station routers) inter-connected using OFDMA wireless links with some of the mesh routers functioning as gateways to a wired network. The objective of our cross-layer formulation is to allow joint determination of power control, frequency-selective OFDMA scheduling and multi-hop routing in order to maximize the minimum throughput that can be supported to all mesh routers. Results of our investigations under typical cellular deployment, propagation and channel model assumptions show that this approach achieves significant mesh throughput improvements primarily due to the following: (a) frequency selective scheduling with OFDMA which provides improved tone diversity thus allowing more efficient bandwidth utilization relative to single carrier methods; and (b) multi-hop routing which provides improved path diversity relative to single hop transmissions.

We quantify the ultimate performance limits of inter-cell coordinatation in a cellular downlink network. The goal is to achieve fairness by maximizing the minimum rate in the network subject to per base power constraints. We first solve the max-min rate problem for a particular zero-forcing dirty paper coding scheme so as to obtain an achievable max-min rate, which serves as a lower bound on the ultimate limit. We then obtain a simple upper bound on the max-min rate of any scheme, and show that the rate achievable by the zero-forcing dirty paper coding scheme is close to this upper bound. We also extend our analysis to coordinated networks with multiple antennas.

We investigate optimum rate assignment scheme maximizing network throughput on the downlink of a multirate CDMA wireless network. Systems employing orthogonal variable spreading factor (OVSF) codes as well as systems employing multiple codes have been studied. Our objective is to maximize the network throughput under constraints on total transmit power, total bandwidth and individual QoS requirements specified in terms of minimum rates. First, users are ordered based on their transmit energy per bit requirements to achieve the target received energy per bit to interference power spectral density ratio at the receivers. Based on the initial ordering, we prove that for systems employing multiple codes, greedy rate assignment yields maximum network throughput. For systems employing variable spreading codes, we show that greedy rate assignment is optimal if the minimum rate requirement of a user is larger than or equal to the minimum rate requirement of any other user with a larger transmit energy per bit requirement. Simulation results verify the superiority of the greedy algorithm under various system and channel assumptions

We propose a rate scheduling algorithm to maximize the network throughput of a variable data rate CDMA system and prove its optimality. The system uses OVSF (orthogonal variable spreading factor) codes and the algorithm finds the optimum rate assignments on the binary code tree under constraints on the total transmit power and minimum QoS (rate) requirement of each user. The algorithm is optimal in the sense that it maximizes the total network throughput within the constraints and achieves this with minimum possible power. The algorithm works in a greedy fashion and has a polynomial time complexity of O(N), where N is the number of users. We also extend our results to a more general set of combinatorial optimization problems where the user rates can be any integer multiples of a basic rate (such as multi-code CDMA), not necessarily the set of rates on the binary tree structure, but the optimal solutions are still greedy achievable.